{ "cells": [ { "cell_type": "code", "execution_count": 892, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, sparse\n", "import bottleneck\n", "def run_egad(go, nw, **kwargs):\n", " \"\"\"EGAD running function\n", " \n", " Wrapper to lower level functions for EGAD\n", "\n", " EGAD measures modularity of gene lists in co-expression networks. \n", "\n", " This was translated from the MATLAB version, which does tiled Cross Validation\n", " \n", " The useful kwargs are:\n", " int - nFold : Number of CV folds to do, default is 3, \n", " int - {min,max}_count : limits for number of terms in each gene list, these are exclusive values\n", "\n", "\n", " Arguments:\n", " go {pd.DataFrame} -- dataframe of genes x terms of values [0,1], where 1 is included in gene lists\n", " nw {pd.DataFrame} -- dataframe of co-expression network, genes x genes\n", " **kwargs \n", " \n", " Returns:\n", " pd.DataFrame -- dataframe of terms x metrics where the metrics are \n", " ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n", " \"\"\"\n", " assert nw.shape[0] == nw.shape[1] , 'Network is not square'\n", " #print(nw.index)\n", " #nw.columns = nw.columns.astype(int)\n", " #print(nw.columns.astype(int))\n", " assert np.all(nw.index == nw.columns) , 'Network index and columns are not in the same order'\n", "\n", " #nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n", " #nw = nw.loc[nw_mask, nw_mask].astype('float')\n", " #np.fill_diagonal(nw.values, 1)\n", " return _runNV(go, nw, **kwargs)\n", "\n", "def _runNV(go, nw, nFold=3, min_count=0, max_count=1000000):\n", "\n", " #Make sure genes are same in go and nw\n", " #go.index = go.index.map(str) \n", " #nw.index = nw.index.map(str)\n", " #nw.index = nw.index.str.replace('_', '')\n", " #go.index = go.index.str.replace('_', '')\n", " #print (nw)\n", " genes_intersect = go.index.intersection(nw.index)\n", "\n", "\n", " #print (genes_intersect)\n", " go = go.loc[genes_intersect, :]\n", " nw = nw.loc[genes_intersect, genes_intersect]\n", " #print (go)\n", " print (nw.shape)\n", " print (go.shape)\n", " sparsity = 1.0 - np.count_nonzero(go) / go.size\n", " print (sparsity)\n", " sparsity = 1.0 - np.count_nonzero(nw) / nw.size\n", " print (sparsity)\n", " #print(nw\n", " #print(go\n", " nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n", " nw = nw.loc[nw_mask, nw_mask].astype('float')\n", " np.fill_diagonal(nw.values, 1)\n", " #Make sure there aren't duplicates\n", " duplicates = nw.index.duplicated(keep='first')\n", " nw = nw.loc[~duplicates, ~duplicates]\n", "\n", " go = go.loc[:, (go.sum(axis=0) > min_count) & (go.sum(axis=0) < max_count)]\n", " go = go.loc[~go.index.duplicated(keep='first'), :]\n", " #print(go)\n", "\n", " roc = _new_egad(go.values, nw.values, nFold)\n", "\n", " col_names = ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n", " #Put output in dataframe\n", " return pd.DataFrame(dict(zip(col_names, roc)), index=go.columns), go\n", "\n", "def _new_egad(go, nw, nFold):\n", "\n", " #Build Cross validated Positive\n", " x, y = np.where(go)\n", " #print(x, y)\n", " cvgo = {}\n", " for i in np.arange(nFold):\n", " a = x[i::nFold]\n", " #print(a)\n", " b = y[i::nFold]\n", " dat = np.ones_like(a)\n", " mask = sparse.coo_matrix((dat, (a, b)), shape=go.shape)\n", " cvgo[i] = go - mask.toarray()\n", "\n", " CVgo = np.concatenate(list(cvgo.values()), axis=1)\n", " #print(CVgo)\n", "\n", " sumin = np.matmul(nw.T, CVgo)\n", "\n", " degree = np.sum(nw, axis=0)\n", " #print(degree)\n", " #print(degree[:, None])\n", "\n", " predicts = sumin / degree[:, None]\n", " #print(predicts)\n", "\n", " np.place(predicts, CVgo > 0, np.nan)\n", "\n", " #print(predicts)\n", "\n", " #Calculate ranks of positives\n", " rank_abs = lambda x: stats.rankdata(np.abs(x))\n", " predicts2 = np.apply_along_axis(rank_abs, 0, predicts)\n", " #print(predicts2)\n", "\n", " #Masking Nans that were ranked (how tiedrank works in matlab)\n", " predicts2[np.isnan(predicts)] = np.nan\n", " #print(predicts2)\n", "\n", " filtering = np.tile(go, nFold)\n", " #print(filtering)\n", "\n", " #negatives :filtering == 0\n", " #Sets Ranks of negatives to 0\n", " np.place(predicts2, filtering == 0, 0)\n", "\n", " #Sum of ranks for each prediction\n", " p = bottleneck.nansum(predicts2, axis=0)\n", " n_p = np.sum(filtering, axis=0) - np.sum(CVgo, axis=0)\n", "\n", " #Number of negatives\n", " #Number of GO terms - number of postiive\n", " n_n = filtering.shape[0] - np.sum(filtering, axis=0)\n", "\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n", " U = roc * n_p * n_n\n", " Z = (np.abs(U - (n_p * n_n / 2))) / np.sqrt(n_p * n_n *\n", " (n_p + n_n + 1) / 12)\n", " roc = roc.reshape(nFold, go.shape[1])\n", " Z = Z.reshape(nFold, go.shape[1])\n", " #Stouffer Z method\n", " Z = bottleneck.nansum(Z, axis=0) / np.sqrt(nFold)\n", " #Calc ROC of Neighbor Voting\n", " roc = bottleneck.nanmean(roc, axis=0)\n", " P = stats.norm.sf(Z)\n", "\n", " #Average degree for nodes in each go term\n", " avg_degree = degree.dot(go) / np.sum(go, axis=0)\n", "\n", " #Calc null auc for degree\n", " ranks = np.tile(stats.rankdata(degree), (go.shape[1], 1)).T\n", "\n", " np.place(ranks, go == 0, 0)\n", "\n", " n_p = bottleneck.nansum(go, axis=0)\n", " nn = go.shape[0] - n_p\n", " p = bottleneck.nansum(ranks, axis=0)\n", "\n", " roc_null = (p / n_p - ((n_p + 1) / 2)) / nn\n", " #print(roc)\n", " return roc, avg_degree, roc_null, P" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:numexpr.utils:Note: detected 192 virtual cores but NumExpr set to maximum of 64, check \"NUMEXPR_MAX_THREADS\" environment variable.\n", "INFO:numexpr.utils:Note: NumExpr detected 192 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" ] } ], "source": [ "from hicmatrix import HiCMatrix as hm\n", "from hicmatrix.lib import MatrixFileHandler\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'hm' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mexp_file_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf'/grid/gillis/data/lohia/hi_c_data_processing/software/CoCoCoNet/networks/mouse_prioAggNet.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mjac_exp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhiCMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mall_genes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mjac_exp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcut_intervals\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mdf_exp_corr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjac_exp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_genes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mall_genes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'hm' is not defined" ] } ], "source": [ "SRP_name='aggregates'\n", "resolution='40kbp_raw'\n", "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/software/CoCoCoNet/networks/mouse_prioAggNet.h5'\n", "\n", "jac_exp = hm.hiCMatrix(exp_file_path)\n", "all_genes = [x[3].decode() for x in jac_exp.cut_intervals]\n", "df_exp_corr = pd.DataFrame(jac_exp.matrix.toarray() , index=all_genes, columns = all_genes)" ] }, { "cell_type": "code", "execution_count": 977, "metadata": {}, "outputs": [], "source": [ "SRP_name='aggregates'\n", "#SRP_name='SRP217487'\n", "resolution='10kbp_raw'\n", "#df_jac_corr_list = []\n", "#for resolution in ['100kbp_raw', '250kbp_raw', '10', 40 , 25, snhic]:\n", "for resolution in ['10kbp_raw']:\n", " exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/{SRP_name}/{resolution}/max/spr/0/all_bins/KR_KR/hic_gene_corr_inter_excluding_intra_nanranked.h5'\n", "\n", " jac_sim = hm.hiCMatrix(exp_file_path)\n", "\n", "\n", "\n", "\n", " all_genes = [x[3].decode() for x in jac_sim.cut_intervals]\n", " df_jac_corr = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes)\n", " #df_jac_corr_list.append(pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes))\n", " \n", " #df_2d_jac, go_chrom = run_egad(marker_table, df_jac_corr_list[7])" ] }, { "cell_type": "code", "execution_count": 920, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'df_jac_corr' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_jac_corr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'df_jac_corr' is not defined" ] } ], "source": [ "df_jac_corr" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "SRP_name='aggregates'\n", "#SRP_name='SRP217487'\n", "resolution='10kbp_raw'\n", "#df_jac_corr_list = []\n", "#for resolution in ['100kbp_raw', '250kbp_raw', '10', 40 , 25, snhic]:\n", "for resolution in ['10kbp_raw']:\n", " exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/{SRP_name}/{resolution}/max/hic_gene_inter_KR.h5'\n", "\n", " jac_sim = hm.hiCMatrix(exp_file_path)\n", "\n", "\n", "\n", "\n", " all_genes = [x[3].decode() for x in jac_sim.cut_intervals]\n", " df_max_gene = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes)\n", " #df_jac_corr_list.append(pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes))\n", " " ] }, { "cell_type": "code", "execution_count": 926, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('chr19', 246282, 246283, b'non-gene'),\n", " ('chr19', 246283, 246284, b'non-gene')]" ] }, "execution_count": 926, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jac_sim.cut_intervals[-3:-1]" ] }, { "cell_type": "code", "execution_count": 921, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " ENSMUSG00000102693 ENSMUSG00000064842 \\\n", "ENSMUSG00000102693 0.0 0.0 \n", "ENSMUSG00000064842 0.0 0.0 \n", "ENSMUSG00000051951 0.0 0.0 \n", "ENSMUSG00000102851 0.0 0.0 \n", "ENSMUSG00000103377 0.0 0.0 \n", "... ... ... \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "\n", " ENSMUSG00000051951 ENSMUSG00000102851 \\\n", "ENSMUSG00000102693 0.0 0.0 \n", "ENSMUSG00000064842 0.0 0.0 \n", "ENSMUSG00000051951 0.0 0.0 \n", "ENSMUSG00000102851 0.0 0.0 \n", "ENSMUSG00000103377 0.0 0.0 \n", "... ... ... \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "\n", " ENSMUSG00000103377 ENSMUSG00000104017 \\\n", "ENSMUSG00000102693 0.0 0.0 \n", "ENSMUSG00000064842 0.0 0.0 \n", "ENSMUSG00000051951 0.0 0.0 \n", "ENSMUSG00000102851 0.0 0.0 \n", "ENSMUSG00000103377 0.0 0.0 \n", "... ... ... \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "\n", " ENSMUSG00000103025 ENSMUSG00000089699 \\\n", "ENSMUSG00000102693 0.0 0.0 \n", "ENSMUSG00000064842 0.0 0.0 \n", "ENSMUSG00000051951 0.0 0.0 \n", "ENSMUSG00000102851 0.0 0.0 \n", "ENSMUSG00000103377 0.0 0.0 \n", "... ... ... \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "non-gene 0.0 0.0 \n", "\n", " ENSMUSG00000103201 ENSMUSG00000103147 ... non-gene \\\n", "ENSMUSG00000102693 0.0 0.0 ... 0.0 \n", "ENSMUSG00000064842 0.0 0.0 ... 0.0 \n", "ENSMUSG00000051951 0.0 0.0 ... 0.0 \n", "ENSMUSG00000102851 0.0 0.0 ... 0.0 \n", "ENSMUSG00000103377 0.0 0.0 ... 0.0 \n", "... ... ... ... ... \n", "non-gene 0.0 0.0 ... 0.0 \n", "non-gene 0.0 0.0 ... 0.0 \n", "non-gene 0.0 0.0 ... 0.0 \n", "non-gene 0.0 0.0 ... 0.0 \n", "non-gene 0.0 0.0 ... 0.0 \n", "\n", " non-gene non-gene non-gene non-gene non-gene \\\n", "ENSMUSG00000102693 0.0 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000064842 0.0 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000051951 0.0 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000102851 0.0 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000103377 0.0 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... ... \n", "non-gene 0.0 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 0.0 \n", "\n", " non-gene non-gene non-gene non-gene \n", "ENSMUSG00000102693 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000064842 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000051951 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000102851 0.0 0.0 0.0 0.0 \n", "ENSMUSG00000103377 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... \n", "non-gene 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 \n", "non-gene 0.0 0.0 0.0 0.0 \n", "\n", "[156467 rows x 156467 columns]" ] }, "execution_count": 921, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_max_gene" ] }, { "cell_type": "code", "execution_count": 335, "metadata": {}, "outputs": [], "source": [ "cpg_list_1 = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/cpg_island/mm10.cpg.20k.txt', sep='\\t', names=['chrom', 'chromStart', 'cpgNum'])\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 336, "metadata": {}, "outputs": [], "source": [ "cpg_list_2 = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/cpg_island/mm10.cpg.20k.txt', sep='\\t', names=['chrom', 'chromStart', 'cpgNum'])\n", " " ] }, { "cell_type": "code", "execution_count": 331, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " chrom chromStart cpgNum\n", "0 chr1 3000000 0.003219\n", "1 chr1 3020000 0.006000\n", "2 chr1 3040000 0.006750\n", "3 chr1 3060000 0.008250\n", "4 chr1 3080000 0.004600\n", "... ... ... ...\n", "133423 KK082441.1 360000 0.006050\n", "133424 KK082441.1 380000 0.005550\n", "133425 KK082441.1 400000 0.007800\n", "133426 KK082441.1 420000 0.005550\n", "133427 KK082441.1 440000 0.006550\n", "\n", "[133428 rows x 3 columns]" ] }, "execution_count": 331, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cpg_list" ] }, { "cell_type": "code", "execution_count": 337, "metadata": {}, "outputs": [], "source": [ "cpg_list_1['bin_range'] = [(int(x/10000)) for x in cpg_list_1['chromStart']]\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 338, "metadata": {}, "outputs": [], "source": [ "cpg_list_2['bin_range'] = [(int(x/10000)) +1 for x in cpg_list_2['chromStart']]" ] }, { "cell_type": "code", "execution_count": 340, "metadata": {}, "outputs": [], "source": [ "cpg_list = pd.concat([cpg_list_1, cpg_list_2])" ] }, { "cell_type": "code", "execution_count": 270, "metadata": {}, "outputs": [], "source": [ "cpg_list = cpg_list.groupby(['chrom','bin_range'])[['cpgNum', 'perCpg', 'obsExp']].sum().reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cpg_list['cpg']" ] }, { "cell_type": "code", "execution_count": 347, "metadata": {}, "outputs": [], "source": [ "cpg_list['z'] = cpg_list['chrom'] + '_' + cpg_list['bin_range'].astype(str)" ] }, { "cell_type": "code", "execution_count": 348, "metadata": {}, "outputs": [], "source": [ "area_dict = dict(zip(cpg_list['z'], cpg_list['cpgNum']))" ] }, { "cell_type": "code", "execution_count": 349, "metadata": {}, "outputs": [], "source": [ "cpg_list_bins = []\n", "for i in jac_sim.cut_intervals:\n", " m_val = i[0] + '_' + str(i[1])\n", " if m_val in area_dict.keys():\n", " cpg_list_bins.append(area_dict[m_val])\n", " else:\n", " cpg_list_bins.append(0)" ] }, { "cell_type": "code", "execution_count": 353, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 353, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min(cpg_list_bins)" ] }, { "cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5129" ] }, "execution_count": 252, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cpg_list['length'].max()" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 245, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min([x[1] for x in jac_sim.cut_intervals])" ] }, { "cell_type": "code", "execution_count": 247, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'non-gene'" ] }, "execution_count": 247, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_genes[-1]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'df_max_gene' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_max_gene\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'df_max_gene' is not defined" ] } ], "source": [ "df_max_gene" ] }, { "cell_type": "code", "execution_count": 909, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3169: DtypeWarning: Columns (15,16) have mixed types.Specify dtype option on import or set low_memory=False.\n", " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n" ] } ], "source": [ "marker_list = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/biccn_cluster_markers.csv')\n", "marker_list['gene'] = marker_list['gene'].str.upper()\n", "#marker_list = marker_list[marker_list['rank'] < 250] " ] }, { "cell_type": "code", "execution_count": 910, "metadata": {}, "outputs": [], "source": [ "df_optimal_marker = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/optimal_number_markers.csv')\n", "df_optimal_marker = df_optimal_marker[df_optimal_marker['n_genes'] >= 10]\n", "df_optimal_marker = df_optimal_marker[df_optimal_marker['f1'] <= 0.8]\n", "#df_optimal_marker = df_optimal_marker[df_optimal_marker['f1'] >= 0.8]\n", "df_optimal_marker = df_optimal_marker.loc[df_optimal_marker.groupby('marker_set')['f1'].idxmax()]\n", "#df_optimal_marker = df_optimal_marker.loc[df_optimal_marker.groupby('marker_set')['f1'].idxmin()]\n", "\n" ] }, { "cell_type": "code", "execution_count": 911, "metadata": {}, "outputs": [], "source": [ "marker_list_optimal_marker = []\n", "for marker, n_genes in zip(df_optimal_marker['marker_set'].tolist(), df_optimal_marker['n_genes'].tolist()):\n", " #print (n_genes)\n", " \n", " marker_list_optimal_marker.append(marker_list[(marker_list['cell_type'] == marker) & (marker_list['rank'] <= n_genes)])\n", " " ] }, { "cell_type": "code", "execution_count": 912, "metadata": {}, "outputs": [], "source": [ "marker_list = pd.concat(marker_list_optimal_marker)" ] }, { "cell_type": "code", "execution_count": 913, "metadata": {}, "outputs": [], "source": [ "df_ensg_name = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/genomes_jlee/mouse_geneid_symbol.txt',sep='\\t', names=['gene_id', 'gene'])\n", "df_ensg_name['gene'] = df_ensg_name['gene'].str.upper()\n", "marker_list = marker_list.merge(df_ensg_name, right_on='gene', left_on='gene') \n" ] }, { "cell_type": "code", "execution_count": 914, "metadata": {}, "outputs": [], "source": [ "marker_table = marker_list.pivot(index='gene_id', columns='cell_type', values='rank')" ] }, { "cell_type": "code", "execution_count": 915, "metadata": {}, "outputs": [], "source": [ "marker_table.fillna(0, inplace=True)" ] }, { "cell_type": "code", "execution_count": 916, "metadata": {}, "outputs": [], "source": [ "marker_table[marker_table != 0] = 1" ] }, { "cell_type": "code", "execution_count": 919, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'df_jac_corr' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 917, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n" ] }, { "ename": "ValueError", "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 6387 is different from 6386)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_2d_jac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgo_chrom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_egad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarker_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_non_bin_corr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_2d_jac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'AUC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'DEGREE_NULL_AUC'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_egad\u001b[0;34m(go, nw, **kwargs)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;31m#nw = nw.loc[nw_mask, nw_mask].astype('float')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;31m#np.fill_diagonal(nw.values, 1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_runNV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_runNV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnFold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36m_runNV\u001b[0;34m(go, nw, nFold, min_count, max_count)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;31m#print(go)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[0mroc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_new_egad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnFold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mcol_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'AUC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'AVG_NODE_DEGREE'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'DEGREE_NULL_AUC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'P_Value'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36m_new_egad\u001b[0;34m(go, nw, nFold)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;31m#print(CVgo)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m \u001b[0msumin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCVgo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0mdegree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 6387 is different from 6386)" ] } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_table, df_non_bin_corr)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 880, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8810606598854065" ] }, "execution_count": 880, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.std(axis=0).median()" ] }, { "cell_type": "code", "execution_count": 879, "metadata": {}, "outputs": [], "source": [ "y = marker_table\n", "#y = marker_table.drop(columns=['Non-Neuronal'])\n", "#y = y.loc[(y.sum(axis=1) != 0), (y.sum(axis=0) != 0)]\n", "#y = y.sort_values(by=['GABAergic', 'Glutamatergic', 'Non-Neuronal'])\n", "genes_intersect = y.index.intersection(df_max_gene.index)\n", "#genes_intersect = marker_list.gene_id\n", "nw = (df_max_gene.loc[genes_intersect, :])\n", "\n", "marker_table = y.loc[genes_intersect, :]\n", "\n", "species= y.idxmax(axis=1)\n", "\n", "lut = dict(zip(species.unique(), sns.color_palette(\"hls\", 85)))\n", "#lut = dict(zip(species.unique(), \"grrbrrryry\"))\n", "lut = dict(zip(species.unique(), \"rgb\"))\n", "#lut = dict(zip(['Brain-Astrocytes', 'Brain-Endothelial cells', 'Brain-Microglial cells','Brain-GABAergic neurons'], sns.color_palette(\"hls\", 4)))\n", "row_colors = species.map(lut)\n", "\n", "\n", "\n", "#nw = (nw.loc[genes_intersect, 'non-gene'])\n", "\n", "nw = nw.loc[(nw.sum(axis=1) != 0), (nw.sum(axis=0) != 0)]\n", "\n" ] }, { "cell_type": "code", "execution_count": 881, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/numpy/lib/function_base.py:2642: RuntimeWarning: invalid value encountered in true_divide\n", " c /= stddev[:, None]\n", "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/numpy/lib/function_base.py:2643: RuntimeWarning: invalid value encountered in true_divide\n", " c /= stddev[None, :]\n" ] } ], "source": [ "nw = nw.loc[(nw.sum(axis=1) != 0), (nw.std(axis=0) <= 1)]\n", "\n", "nw.std(axis=0).median()\n", "\n", "nw.shape\n", "\n", "import h5py\n", "import scipy.sparse as ss\n", "import os\n", "import sys\n", "import numpy as np\n", "import pandas as pd\n", "from hicmatrix import HiCMatrix as hm\n", "from hicmatrix.lib import MatrixFileHandler\n", "from itertools import combinations\n", "from scipy.sparse import csr_matrix, dia_matrix, triu, tril, coo_matrix\n", "from sklearn.metrics.pairwise import pairwise_distances\n", "from scipy import stats\n", "import pyranges as pr\n", "\n", "\n", "def pearson_corr(arr):\n", " \n", " #pearson_matrix = np.corrcoef(arr.toarray())\n", " pearson_matrix = np.corrcoef(arr)\n", "\n", " return pearson_matrix\n", "\n", "rank_abs = lambda x: stats.rankdata(x)\n", "arr2 = np.apply_along_axis(rank_abs, 1, nw.to_numpy())\n", " \n", "\n", "nw_pearson = pearson_corr(arr2)\n", "\n", "nw_pearson.shape\n", "\n", "df_non_bin_corr = pd.DataFrame(nw_pearson , index=nw.index.tolist(), columns = nw.index.tolist())" ] }, { "cell_type": "code", "execution_count": 902, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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{}, "output_type": "execute_result" } ], "source": [ "nw.index.tolist()\n", "\n" ] }, { "cell_type": "code", "execution_count": 601, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", " warnings.warn(msg)\n" ] }, { "data": { "image/png": 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k/Srfny3pG3XX8KSk9+br0yUtkPRAXs/eleNcIOmxyrWeXjnGKEmPSFpYu4cc31LSrJw3S9J7KttOy/0fkXRwZXyvvLeFkn6gfEhN0kaSpuX4nZK2r8wZm+d4TNLYyvinJN2bn9MUSYNyfD9JSyv3cmaOf7Du5/GSpH/JbdMq409Kui/HN5R0RV7z/ZL26+rn3tRVLCSNAr4PDAR+HBET1vMlmZmZWd/yakTs0WDbEEmHRMTMuvFTgOciYjcoQR3wRm57GdhV0iYR8SrwaeBP3bkQSfsChwF7RsSKDJo3zM3nAe8HdouI1yQNBr6e8wYCP8xztQJ3S5oREQ8C44FbI2JCBs7jgVMl7QyMAXYB/gK4RdJOEbEKmAgcD9wB3ACMAmYC44AlETFc0hjgQuBoSVsCZwEtQAD3SJoBLAWmAAdExKOSzgHGApfnPf02Ig6rfgYR8QiwR+W+/gT8MrcdXfms/j2PD/CV3L6bpCHATEkfjYi2Rp/1Gq0gS+pVX11ca+0/hkOAnYFj8odtZmZmtjZcBJzRwfhQKkFvRDwSESsq22cCn8nXxwA/6+b5hgKLaseKiEUR8YykTSlB4D9HxGu5bVlEnJ3zRgILI+LxiHgdmAqMzm2jKUEq+f1zlfGpEbEiIp4AFgIjJQ0FNo+IORERwFV1c2rHuhY4IFeXDwZmRcTiiFgCzKIE1VsBKyLi0ZwzC/i7bn4WAAcAf4iIp6qDec7P0/657gzcmp/L88CfKcF6Q2uYYtG7vrrQ2X8MZmZmZt2xSd2f9I+ubJsDrJC0f92cyZRV2DmSzpM0om77VGCMpI2B3YE7u3ktNwPbSnpU0o8kfTLHhwNPR8SyBvOGAX+svG/NMYCtI+JZgPw+pIs5w/J1R8d6c05ErKSs4G7VybEWARtIqgWrRwLbVvbbN1MiZkrapYP7GkPHv1x8grKC/1i+vx8YrZKGsgOwV9153qJP10FuaWmJDobfAAbuuOOOA5YuXfrmPttvvz0vv/wyLS0tF+R+QZNXrps7d26H97fwV2M6+ty6bfhhU99y3NdW0qNj9iVf/fkDXHbU7uv7Msysn4vww/NrwyYb6ARKukDNpIiYVHnfWYoFlNSGM4BTawMRcZ+kHYGDgAMpKQ37RsRDuf2BzM89hpKiUNXo39OIiOWS9qIEgPsD0zIt4t7qjpKOo6R5bAV8jI7jna7+3W40p7NjrdGciIhMxfiupI0ovwCszO33An+Z93wo8L+BN3/RkLQh8FngtA6OXb8qPxn4MDAXeAr478p5OrRGAfImG/SugPKee+45ngb/UUs6Cjh40aJFX873XwRGPv/88/9cPUZLS8t4YON8+xfAMx2c6lBgN0qezxqtuvdGj3+oZ/OHr53L6JW68w+Og2PryCW/e5x//usd1/l5Vq4KBg0Uq9qCgX3twZA+oK0tGNBHPlcHx2tHxg2Tutyx8fzbJJ0L7FM3vhy4DrhOUhsllniosssM4GJgP0ogW/MiJZWiajAlLYDMAZ4NzJY0j5KzOx3YTtLgTK24ArhC0nzKc1itrL5iug3t8c5zkoZGxLOZPvF8jjea05qvOzpWbU5rPmy3BbA4x/ermzM772cOJeBH0kHATjn+Um3niLghV8zfGxGLcvgQ4N6IeK76QeV5j6CsEtfmrwT+tbLPfwOP0Yk1CpB7my7+o+7sP4Y3zZ07tzsP7p3daEMlwP4LStL4R3PTa7QH1LUk8O7+72yd/m9vx1t6eIAmjpD9D469Xe9EcAwwaGD5j9TB8brRV4Jj63XOBy4DHgeQ9HHgwYhYkiudO5MBYcVkYGlEzKurqnA7cI2kCRGxTNIRwP0RsSof9murpA7sATwVEa9Iuhy4VNIJ+ZDeQNof4LsbGJHpBX+ipCZ8IbfNoATZE/L79ZXxn0r6DiXGGQHcldexTNI+lNSQY4FL6o41h5IucVuuEt8EXKD2ChkHkSu/koZExPO5gnxqfpZIej8lTSIkjaTEUy9WPqdGudsHAg9HxJtpIJmjrYh4WdKngZX5gGJDfTpA7kJn/zGsNd0JsDOIHksJ0gexdgLggbzdn9+LXe9iZmZmQOYgV97fGBHjqzvkCucLlaEPABPzYbEBwK+BX9TNaaVU2qJu/AFJlwK/kxSUFd0v5+bNgEskvZuSIrCQ9r+knw6cC8yXtAx4lfLA3DMRsVLSycBNlPhhckQsyHkTgOmSxgFPA0fldSyQNB14MM91Uq5eA5wIXAlsQnngsFbF43LgakkLKSvHY/JYi3OV/e7c75yIWJyvvynpsPycJkbEbTl+JHCipJV5L2PyocBawPtp4IT6z4+O85KHADflSv6fgC92MG81ynM1pcxZ+R7t/zGcvz6vJwPlHYHtKA8RDqY9yF2TVeZuBdiNcpDHLJzcox/61OFf6tc5yNZ/9aa0hrYI14M3Wws2HtS70ketd2jmFWQi4gbemvy+3nS22pzB8xHAX7GOfy7bc3MPj/CltXIdZn1NbwmOAQfHZmbrUFOvIPdFLS0tkyg5RR/mrekYA/N97U82nWpYxWLMnT2rYjF173dkBfkPz73MB7Z+19o+7Dti6StvsMWmG6zvywDgtTdWsfEGA9f3ZZiZ9UpeQbaONPUKcl80d+7c4+HNFeUTKXUC1250s993e3iAqWvlMrqyvoLjt1M66Y2VbWwwqP13lt4SHAP9Ijjub+WunF7RP/S3/67NehOvIPcBLS0tDwE7ABvw1vzjhv/7bLSCPGHMwh790MdPHe4cZDMzawpeQbaOeAW5D5g7d+6HKyvKfwGsqmyuVbPoduOTI/frqCvmmnhnVpDNzMzM1gcHyH1EPuA3IVeTP8garCTXu/HAnl3LyT2bbtZtf375Dd79rt6TrmJm/Y+kVcC8ytDUiJggaTawWUS05H4twMURsV+WIftPShtpUZp8jMqucAH8JCK+mPMGAc8Cd0bEYZLOBpZHxMWVa3gSaImIRZJOp5StXUWpgHVCRNyZxzmHUqbt5Zz681oFL0mjKGXlBgI/jogJOb4lMA3YHngS+HxELMltpwHj8lxfi4ibcnwv2su83QCckvWKNwKuojTpeBE4OiKezDljKR0HAc6LiCk5fg3QQumEfFfezxuStgB+Qqn8NSg/2yuyPfftwEY5fm1EnJXH+gilHvVmeS9/HxEvZS3q/8jztOX1zn7rT7udA+Q+JleT/w+lEPbGXe3fkVGrut5nXVrxRhsbbdB7GxI676/3aNbguDeVizOzLnXWanqIpEMiYmbd+CmUJhe7AWSDjzdy28vArpI2iYhXKfV8/9SdC5G0L3AYsGdErJD0XtqbgZwHvB/YLRuFDAa+nvMGAj/Mc7VSWl/PyGYZ44FbM+gfn+9PlbQzpabwLpS/Xt8iaaeshTyRUn/5DkqAPIpSC3kcsCQihmcL6QuBozMIP4sSoAZwT55/CXAN8A95Dz+l1HyeCJxEabbyt5LeBzySwfQK4FP5y8YGlHrRMyPiDuDHwDci4jeSvgR8E/gW8BWAiNhN0hBgpqSPRkStxO5bOEDug+bOnfu3LS0tL/E2A2S+18MLmNiz6b05OIa1FxzXP7hnzaWtLZDU4X8vXT1EVw2Oe9ra+PWVbWzo/876Jf8y37WuPqO2tm5nJzZyEWVVtD5AHgo81X4d8Ujd9pnAZ4Brae8I94lunG8osCgiVuRxF8GbjTO+AmwfEa/ltmW0dwIeCSyMiFqnv6nAaEoTkNG0t4GeQun4d2qOT81zPZHNP0bmavbm2SIaSVcBn8t7Gl0557WUzn4CDgZm1ZqDSJpFCap/liV5yfG7aG9jHcDgnL8ZpfHIymwWsjz32SC/as9BfZCyugwwi9IY5VuUToa35ufyvKQ/U4L1uxp90A6Q+6i5c+du3tLS8jrlP4w1cvfXe3buJu40vVY5OG5unQW1a1JhoqetjR0c918OjrvW1Wc0YICQdDzt3egAJkXEpMr7+k56346Iafl6DnC4pP2BZZV9JgM3SzqSEphNqbSHhvIwz5mSfkVJw5hM9wLkm3Peo8AtwLSI+A3ln+anMyjuyDDgj5X3rcDe+XrriHgWICKezRXW2pw76uYMo6yEt3Ywvtp5snvfUmCrBucfVnlPrgZ/kbL6DnAppXX1M5TGakfXVnxzRfyevO8fRsSdOWc+8FlKu+yjgG1z/H5gdP5isC0lBWRbHCA3rZso7RNfzfd7041V5Y/+ew/P2sMV5N5iXa++rFwVDBq4/v8Fe/X1VWyyYfOXejMzezsyGJ7UyS6dpVhASW04g7LqWjvmfZJ2BA6ipETeLWnfiHgotz8gaXvK6nF9Q7NGVaEi0wr2ogTT+wPTMi3i3uqOko6jBJpbAR+j42XyrqpPNZrT2bHezpyaHwG3R8Rv8/3BwH3Apyitu2dJ+m1EvJRpHntky+1fSto1IuZTOpn9QNKZlOD69TzWZEp/ibmUlf3/prTPbsgBch+WqRavsoapFl5BLtb16ktvCI4BB8fvoO7UJ/afxc2aS0TcJulcYJ+68eXAdcB1ktqAQ4GHKrvMAC6mpDdsVRl/kZJKUTWY8qAfGRzOBmZLmgeMBaYD20kaHBHLIuIK4ApJ8ykP5bXSvpoKJY3hmXz9nKShuXo8FHg+xxvNaaU9DaL+WLU5rfnQ4BaU1IhW2tM4anNm195IOgt4H3BCZZ/jgAmZUrFQ0hPAh6is+kbEn/NhyVHA/Ih4mPJLCZJ2oqSxEBErgX+tnO+/geqK/ls4QO771vhneP2q83p0wmN6NNuseXUntcLBsVlTOp9SPaGW4/txygNmS7KCws5UAsI0GVgaEfMk7VcZvx24RtKEiFgm6Qjg/ohYlQ/7tVXSNfYAnoqIVyRdTsn5PSEf0htI+wN8dwMjJO1AeSBwDKUSBpRAfSwwIb9fXxn/qaTvUB7SGwHcldexTNI+wJ3AscAldceaAxwJ3JbVLW4CLpD0ntzvIOC0/Ky+TFktPqDuobmngQOA30rampJf/Hg+sPdGBsebUFboL8xjDckc4wGUVf3LcnxTSu+PlyV9mpLL/CCdcIDc9906d+7cUS0tLXdRSqFsSRd5yed9r4d1kCe6DrKZ2frmv0a8Y+pzkG+MiPHVHSLiBkkvVIY+AEzMB8wGAL8GflE3p5VSdo268QckXUqpzhCUFd0v5+bNgEsytWAlsJD2/OnTgXOB+ZKWUdIvpwDPZD7wyZTUzIHA5IhYkPMmANMljaMEpUfldSyQNJ3yIN9K4KRcvYbSl+FKSpm3mbQ/pHg5cHU+0LeYEogTEYtzlf3u3O+c2gN7lCD2KWBO+bi4LiLOyXu5MlfJBZyaZe52B6bkLwADgOkR8as81jGSTsrX1wFX5OshwE25kv8nSq5zp9xJr4/rpC5yw056Ny8c06Mf+kHDp7qTnpmZNQV30rOOeAW5j8u6yE9ScpaGA5t3NWdy17t06qAezTYzMzPr3RwgN48PU/7U0aXzbnmpZ2dqlqf0zPqY3lIZxcys2TlAbg6bU/JwVtKdn+n+6/pyzGxdcHBsZvbOcIDcHDanJN13z/d6eLYmqYNs1pe5XbWZ2brjALk53EvpxLMh3eiZ6TrIZj2z7LWVDN747f3vc21VHnBwbPbOkLQKmFcZmhoRE7L+7mYR0ZL7tQAXR8R+WVbsPyn/NotSw3hUNvoI4CcR8cWcNwh4FrgzIg6TdDawPCIurlzDk0BLVnE4nVKibRXQBpwQEXfmcc6hVKF4Oaf+PCLOz2OMolTNGAj8OCIm5PiWwDRge+BJ4PMRsSS3nQaMy3N9LSJuyvG9aK9icQNwSpZz2wi4itKp7kVK97snc85YSuk1gPMiYkqOX05p+yzgUeAf83MaTalk0Ub5C/m/RMTvcs4plNbaAv4zIr6X43tQqmJsnHP+KSLuyqYsDwG1lt93RMRX6YQD5CYwd+7ckS0tLS/RXu+wU1t1vYutBb1xha87jSzsreqD2rcbHEP3g2P/rMx6jc466Q2RdEhEzKwbPwV4LiJ2A8j6xW/ktpeBXSVtEhGvAp+mlB7rkqR9gcOAPSNihaT30v5v/3nA+4Hdsg7yYODrOW8g8MM8Vyuls9+MrAU8Hrg1g/7x+f5USTtTyrTtQqmDfIuknbLU20RKebk7KAHyKEqpt3HAkogYLmkMpT7x0RmEn0UJhAO4J8+/BPjXiHgpr/M7wMmU0nO3AjMy8N6d0gzlQ5J2pQTHIymd8m6U9OusDf2/gP8ZETMlHZrv98vP5w9ddERcjQPk5tHtFIsd3Wr6HdHbgmPoXiMLe6v18bH5Z2XWJ1xEWRWtD5CHUmr7AhARj9Rtn0np8nYtpf/Wzyjto7syFFgUESvyuIvgzUYYXwG2j4jXctsy4OycNxJYGBG1RiZTgdGUGsejaQ8ip1Aampya41PzXE9kbeORuZq9eUTMyWNdBXwu72l05ZzXUhqXiNIIZFat9rGkWZSg+meV4FiUFenI619eue930d6a+sOUFeBXct5vgMMpwXDQXs1rC9o7/K0xB8jNYxnlt9Na55zGzUKOfIeuyMzMrJeTdDztzTYAJkXEpMr7+kYh346Iafl6DnC4pP0p/w7XTAZulnQkZSV0SqX7HcBU4ExJv6KkYUymewHyzTnvUeAWYFpE/IaS/fh0BsUdGQb8sfK+Fdg7X28dEc8CZLvpIZU5d9TNGUaJNVo7GF/tPNmcZCnlD9cdnb82B0lXUFpxP0iueuf44cC3KY0+PpPD84HzJW1FaYZyKDA3t/0LpSHIxZTiBR+rnHMHSb8HXgLOiIjf1n9IVQ6Qm8d76GaKBdf28EwH9HB+L+Y/a5uZ9S8ZDE/qZJfOUiygpDacQVl1rR3zPkk7UloHHEhJadg3Ih7K7Q9kXuwxlBSF1S6p8aXG8sz//QSlJtW0TIu4t7qjpOMoaR5bUYLEjv5h66rBV6M5nR3r7cwhIo7LNJBLgKPJDngR8Uvgl5L+hpKPfGBEPCTpQmAWsBy4n5JvDKXD379GxC8kfZ7S2e9ASo73dhHxYn5+/1vSLrXV6444QO6PPrK+L6B3qeaXNmNw/Nobq9h4g+4XOTEzs+6LiNuyjfI+dePLKe2Or8sWx4dSHhSrmQFcTElvqD4e9CIllaJqMOVBPzIHeDYwO9swj6Xk524naXBELIuIK4ArJM2n/GW5Fdi2crxtaE8/eE7S0Fw9HkppbU0nc1rzdUfHqs1pzYcGt6C0nG6lPY2jNmd29QYjYpWkacA3aW8RXdt2u6QPSHpvRCyKiMspwS+SLqB9RXss5RcDgJ8DP875K4BaWso9kv4A7ET7yvNbOEBuHlOA4+jGz/TuA3t2omarYtGEMfFqHBybma1z51OqJ9RyfD8OPBgRSyRtCOxMXUBISatYGhHzJO1XGb8duEbShIhYJukI4P4MID8ItFXSNfYAnoqIV7IaxKWSTsiH9GoplwB3AyMk7UB5IHAMpRIGlEB9LOXBuLHA9ZXxn+aDc38BjADuyutYJmkf4E7gWMrKb/VYcygJnbflQ3Y3ARdIek/udxBwWuYdfyAiFubrvwUezs9wOOXBupC0Z97Li7ltSEQ8L2k74Ahg3zzuM8An87P+FPBY7v8+YHFe+455L4/TCQfITWLu3LnHt7S0/DUlT2cLOnlo76MP9/BkzRYhm5mZNVafg3xjRIyv7hARN0h6oTL0AWBiBn0DgF8Dv6ib00opu0bd+AOSLgV+lyXhnge+nJs3Ay6R9G5KWsFC2vOnT6ekIcyXtIySnzsFeCbzgU8GbqLEB5MjYkHOmwBMlzQOeJpSJo6IWCBpOiUveCVwUq5eQ0lluJLyUN1M2h9SvBy4Oh/oW0wJxImIxbnKfnfud06ODQCmSNqckoZxfx4b4O+AYyW9kfdydETU0jJ+kTnIb+R1LcnxrwDfz9Xr1yqfzd8A50haSSlZ99XaA4ONqP1c1te1tLS8CmxE5vrMnTu3w7XRhSeO6dEPffjEqW857msru8xlMrNu6igXvq0tGNALK6OY9XUbD+q6f4D1P15Bbi4D6UajEI5d9xdiZl1rFPR2lAvv4NjM7J3jALm5LAfeTVdB8lU9PMu+Xe9iq3t9ZRsbDhqwvi/DepmeBL1eUTYzW3ccIDeXjbqz0+NuNf2Oc3Dcc2+samODgX3/c1xbrab7Q3DsXwLMbH1xgNx8uvzX5MV34irM1rJmCI6h+aumrE0Ojs1sfWmOf3GsZhmwlPLkZsOH5v6rh19m60JbDx4Y7i3PGvfkHtbF+draeskHY9aHSVol6b7K1/gcny1pbmW/Fkmz8/Wmkq6RNE/SfEm/k7RZbgtJV1fmDZL0QnbVQ9LZkr5Rdw1PSnpvvj5d0gJJD+T17F05zgWSHqtc6+mVY4yS9IikhbV7yPEtJc3KebMqpdiQdFru/4ikgyvje+W9LZT0g6zWgaSNJE3L8TuzGUptztg8x2OSxlbGr5T0ROWa98jx0ZV7nCvpr+s+k4GSfl/73HJsWuU4T9aqj0j6+7qfYVvtPI14Bbm5dKub3r+d18OzXNnD+WYd6EmTlt6yKvtON5rp6nxegTVbKzrrpDdE0iERMbNu/BTguYjYDUClfvEbue1lYFdJm0TEq8CnKbWJuyRpX+AwYM+IWJFBc+3f/fOA9wO7ZR3kwWTbZpWayD/Mc7VSOvvNiIgHgfHArRExIQPn8cCpknamlGnbhVIH+RZJO2Wpt4mUEmp3UDoBjqKUehsHLImI4ZLGABcCR0vaEjgLaKEs4N2T56+VZ/tmRNT3+b0VmJF1kHenNEP5UN1n/BCweW0gIo6ufFb/Tlk0JCKuAa7J8d2A6yPivs4+awfIzWUlsAFdpVns0+lWe5tefX0Vm2w40O2qzcz6j4sobabrA+ShwFO1NxHxSN32mcBngGsp7aZ/Rmkf3ZWhwKLsDEdELIKyYk2pAbx9RLyW25YBZ+e8kcDCiKg1MpkKjKbUOB5Ne5e7KZQmG6fm+NQ81xNZ23ikpCeBzSNiTh7rKuBzeU+jK+e8ltK4RMDBwKxa7WFJsyhB9c8a3Wh2Iqx5F5W/jEvahvL5nQ/8j/q5ec7PU5qF1Kt93p1yikVzuZ/Sb7xtfV9If7TJhqU3i4NjM7PepbN0JEnH55/wa1/H1+2ySd2f54+ubJsDrJC0f92cyZRV2DmSzpM0om77VGCMpI2B3Skd6brjZmBbSY9K+pGkT+b4cODpDIo7Mgz4Y+V9a44BbB0RzwLk9yFdzBlGe2vn+mO9OSciVlJWcLfq4vwA52c6xXclvVlwQNLhkh6mNFr5UmX/7wH/RuN45xOUFfzHOth2NN0IkL2C3Fx2pXTZ6TxCu/8duRYzM7NeobOFi4iYBEzqZHpnKRZQUhvOoKy61o55n0pL44OAAykpDftGxEO5/YHMzz2GkqKw2iU1vtRYLmkvSgC4PzAt0yLure4o6ThKCsJWwMfoOC7o6iGFRnM6O9bbmXMa8P8oqSKTKJ/jOQAR8Uvgl5L+htIl8EBJhwHPR8Q9Wr1Fd1WHq8SZr/1KRMxvMO9NDpCby0rgdcp/ZI3/b3DCO3U5ZmZmzS0iblNpo7xP3fhy4DrgOkltwKGUnNmaGcDFlPSGrSrjL1JSKaoGA3/O466ipEHMljQPGEvJz91O0uCIWBYRVwBXSJpPaSLWCmxbOd42wDP5+jlJQyPiWUlDKa2t6WROa77u6Fi1Oa0q7Z63oLScbqU9jaM2Z3bez7M5tkLSFcBqDyjmPrdL+kDmXH8c+KykQ4GNgc0l/SQi/gHKw4rAEcBe9ceh5FR3uXoMDpCbzbWUBPzt6CxA/o8enmViD+f3YmurRq1ZI/5vzKwpnQ9cBtRyfD8OPBgRSyRtCOxMBoQVk4GlETGvbiX0duAaSRMiYpmkI4D7I2JVPuzXVkkd2AN4KiJekXQ5Jef3hHxIbyDtD/DdDYyQtAPlgcAxwBdy2wxKkD0hv19fGf+ppO9QHtIbAdyV17FM0j6U1JBjgUvqjjUHOBK4LR+yuwm4QO0VMg6irBxTCc5FyWWen+PDgT/k/D3zXl6MiNMqc/cDvlELjtOBwMMRUU0DQdIA4Cjgb+gGB8hNZO7cuce3tLSswrnlb1uzBi61Bwht/WvW/8bMmtgmtXJh6caIGF/dISJukPRCZegDwMQM+gZQcmh/UTenFfh+/cky/eJS4HeSgrKi++XcvBlwiaR3U/5qvJBSTQLgdEoawnxJy4BXKQ/dPRMRKyWdDNxEWVGeHBELct4EYLqkccDTlCCSiFggaTrlQb6VwEm5eg1wIqWm1SaUh/NqDyleDlydD/QtpgTiRMTiXGW/O/c7p/bAHuWXgfdRFvbuA76a438HHCvpjbyXoyO6VUuz0Srx3wCttQcVu6Luncv6ipaWlpcoHfU2mDt3boeB8sLLxvTohz78q1Pf8k/8ayu7zGVqWs3S4c3MrD/aeFDXDbas//EKcvOp/kmlQ2ccuHlnm7s0tUezm09vDI5XtQUDswbuijfa2GiD3neNZmZmvZUD5ObzZ0pHva0b7XDeLS/17AzDezbd1r2BlQYR/pO+mZnZmnGA3HwGA5t2tsMZL/aslZ5XkPuWDQd59djMzGxNOEBuPhtT0iwaOq/1jB6ewiGymZmZNS8HyM3nNUqA3HDZ8PGv9+wEzrBoDq5sYWbWNUmrgHmVoakRMUHSbGCziGjJ/VqAiyNiv2z9/J+ULnmipD+OykYfAfwkIr6Y8wZRuuDeGRGHSTobWB4RF1eu4UmgJSIWSTqdUqJtFaWT3AkRcWce5xxKFYqXc+rPI+L8PMYoStWMgcCPI2JCjm8JTAO2B54EPh8RS3LbacC4PNfXIuKmHN+L9ioWNwCnZDm2jYCrKDWIX6RUnngy54ylNFQBOC8ipuT4lcAnKV33AP4xG61sAfyEUrp2UH62V+Scf6VU9oj82RyXpe0+Qim3t1ney99HxEuS/h74ZuVnuDuwZ0TcRwMOkJvPfOAlSh3ADu347z08QxPXQe5PHByvP9WHKM2s1+usk94QSYdExMy68VMorY53A8j6xW/ktpeBXSVtEhGvUvoX/Kk7FyJpX+AwSnC3Ihtn1B7MPw94P7BbBouDga/nvIHAD/NcrZTOfjMi4kFgPHBrBv3j8/2pknamlEzbhVIH+RZJO2Wpt4mU8nJ3UALkUZRSb+OAJRExXNIY4ELg6AzCzwJaKEHtPXn+JXnt34yIa+tu9yRKLem/zTJwj0i6Bngf8DVg54h4NUvRjaEE7D+m1EX+jaQvUYLib0XENcA1+VnsBlzfWXAMDpCb0SBKW8mG0Y9XkM3WLwfHZk3jIsqqaH2APBR4qvYmIh6p2z4T+AylwVetLfInunG+ocCiiFiRx10EkCvWXwG2j4jXctsy4OycNxJYWKsBLGkqMJpS43g07V3uplAampya41PzXE9kbeORuZq9eUTMyWNdRWnwMTPn1M55LaVxiYCDgVm12seSZlGC6s662gUwOOdvRqmrvDK3DaLUp36D8txVrZPfBymNVgBmUeo+f6vuuB22oa7np3eazx6UB/UaerGHX9b3vb6ybX1fgplZryDpeElzK1/H1+2yiaT7Kl9HV7bNobRI3r9uzmTKKuwcSedJGlG3fSowRtLGlD/339nNy70Z2FbSo5J+JOmTOT4ceDqD4o4MA/5Yed+aYwBb19o95/chXcwZlq87OtabcyJiJSVtYqsuzg9wvqQHJH030zQALgU+TAl+51HSONoi4k+UFt1PU1JTlkbEzTlnPvDZfH0Uq7fKrjmabgTIXkFuPs9S/mPcpNEOH72lh2fwEnKf58oWfd/KVcGggV6JNuupiJgETOpkl85SLKCkNpxBWXWtHfM+STtSWiofSElp2DciHsrtD0janrKaeUP9JTW+1Fie+b+fAPYHpmVaxL3VHSUdR0nz2IryV+WO/mfRVYOvRnM6O9bbmXMa8P8oqSKTKJ/jOZRV5/uAT1E6E86S9FvKX8hHAztQcrt/LukfIuInwJeAH0g6k9L2+vXVbkjaG3glIuZ3cD2rcYDcfIYCG3S2w9KG2clm1ltEdF7DuhYcO5/ZbP2KiNuyjfI+dePLgeuA6yS1AYcCD1V2mUFZCd2PEsjWvEj5t7xqMCUYJHOAZwOzJc0DxgLTge0kDY6IZfkw2xWS5lMCylZWX03dhva0hOckDY2IZyUNpbS2ppM5rfm6o2PV5rTmQ4NbUFIjWmlP46jNmZ3382yOrZB0BfCNfH8cMCHbSy+U9ATwIeAvgSci4gUASddRfgn4SUQ8TPmlBEk7UdJYqhq1oX4LB8jN53W6qGKxhR/SM+v1utvgJfptk3ezXuV8SvWEWo7vxykPmC2RtCGwMxkQVkympAfMk7RfZfx24BpJEyJimaQjgPsjYlU+7NcWEY/lvnsAT0XEK5Iup+T8npAP6VU7694NjJC0A+WBwDGUShhQAvWxwIT8fn1l/KeSvkN5SG8EcFdexzJJ+1BSQ44FLqk71hzgSOC2rG5xE3CBpPfkfgdRVo6pBOei5DLXVnefBg4Afitpa0p+8eOU1eh9Mu/61dxnbh5rSEQ8L2kAZVX/stqHmmNHAX9DNzhAbj5tdJFbfrcf0uu32iIY4NZ6TcVpFs3P7eLXu00k3Vd5f2NEjK/uEBE3SHqhMvQBYGIGfQOAXwO/qJvTSim7Rt34A5IuBX6XJeGep5Q0g/Kw2iWS3k15YG0hpZoEwOnAucB8ScsoweMU4JmIWCnpZMpDawOByRGxIOdNAKZLGkcJSo/K61iQFSIezHOdlKvXACfSXuZtJu0PKV4OXJ0P9C2mBOJExOJcZb879zun9sAe5ZeB91EC3/uAr+b4ucCVuUou4NR8KHGRpGspaSUrgd/TniJzjKST8vV1wBWVj/ZvgNbag4pdUXj5oam0tLT8N/BXwMZz587t8F/OhSeO6dEPffjEqW857msru8xl6jcchNo7pa0tGOD0ivXOP4e+beNBHebHWj/nFeTm81eUbnoNeQV53XonguOu8lOtf3BQ1jv452DWfBwgN6dGT4wC8NEf9fDo3+nhfOsxB8dmZmbrjpOams8tlFqDq7ra0czMzMzeygFyk5k7d+7fUop8N+4j/GoPv8zMzMyamAPk5tR56syRPfwys3XOz0+b9Q6SVtV10huf47Mlza3s1yJpdr7eVNI1kuZJmi/pd5I2y20h6erKvEGSXpD0q3x/tqRv1F3Dk5Lem69Pl7QgO8/dl80vase5QNJjlWs9vXKMUZIekbSwdg85vqWkWTlvVqUUG5JOy/0fkXRwZXyvvLeFkn6Q1TqQtJGkaTl+ZzZDqc0Zm+d4TNLYyvjJuX/U7jHH3yPpl3mfd0natbLt3ZKulfSwpIck7ZvjH1HpXjhP0v+RtHmObyjpihy/v66sXoecg9ycOu8j/FinW7t2QA/nm/Vj3X3A0nnmZr1GZ530hkg6JCJm1o2fAjwXEbsBZP3iN3Lby8CukjaJiFeBT1NqE3cpA8HDgD0jYkUGlLVax+cB7wd2yzrIg4Gv57yBwA/zXK2Uzn4zIuJBYDxwa0RMyMB5PKVN9s6UMm27UOog3yJppyz1NpFSXu4OSifAUZRSb+OAJRExXNIY4ELgaElbAmcBLZTnpO7J8y8B/i/wK95aJ/r/A+6LiMMlfSivvxaBfJ9Sbu9IlTrTm+b4j4FvRMRvJH0J+CbwLeArABGxm6QhwExJH42IhvGSA+Tm9DiloHbH7n/nLsSsP+lOib+eBL5vrGpjg4H+w59ZL3IRpSFFfYA8FHiq9iYiHqnbPpPS5e1aSrvpn1HaR3dlKLAoIlbkcRdBWbGmBIHbR8RruW0ZcHbOGwksrNUAljSV0q75wfy+X+43hRKonprjU/NcT2Rt45GSngQ2j4g5eayrKA0+Zuac2jmvpTQuEaVt9Kxa7WNJsyhB9c8i4vc5Vn+vOwPfznt5WNL2Kg1DXqXUNP7H3PY67S2lP0hptAIwi1L3+Vt5rFtz/+cl/ZkSrN/V6IN2gNycdqKTKhYu81a4XrGtbev6vycHx2Zrn6TjaW+2ATApIiZV3tc3Cvl2REzL13OAwyXtDyyr7DMZuFnSkZTAbEql+x3AVODMTKvYPffvToB8c857lPJQ/rSI+A3ln+anMyjuyDDKA/w1rcDe+XrrWrvn7Gg3pDLnjro5wygr4a0djK92nmxOspTSRruj8w+jc/cDR1AapoyktJjehlKE4AVKK+2PAPcAp0TEy5QufJ+ldAM8ivZW2fcDo/MXg22BvfJ7wwDZ/7fthzbp4VezcHBs3bVy1bpJCF6TPOO2Niclm60LETEpIloqX5Pqdnk1IvaofE2r234eZRW5esz7gB0pK8xbUlIaPlzZ/gCwPWX1+Ib6S2p8qbGcEtwdTwkSp0n6x/odJR2XOch/lLQtHS+adfU/lUZzOjvW25nTyATgPfnLyT9TOuatpCzu7glMjIi/oqSs1HKqvwScJOkeYDDtK8uTKUH5XOB7wH/nsRryCnI/NHX5eT2a/7m1cxlmfca6aue8Jr+juRmFWe8UEbeptFHep258OaXd8XWS2oBDgYcqu8wALqakN2xVGX+RkkpRNRj4cx53FSUNYrZKG+axwHRgO0mDI2JZRFxBWWGdT6lq1Ur7aiqUldhn8vVzkobm6vFQSmtrOpnTmq87OlZtTqukQcAWlJbTrbSncdTmzKYTEfEScBxApmk8kV+bUlpG35m7XksGyBHxMHBQztmJksZCRKwE/rV2bEn/TRdPZDlA7ofO3OyMrnfq1NS1ch1mZmZN4nzgMsozQEj6OPBgRCzJh8h25q0B4WRgaUTMq6uqcDtwjaQJEbFM0hHA/RGxKh/2a6uka+wBPBURr0i6nJLze0I+pDeQ9gf47gZGSNqB8kDgGOALuW0GJciekN+vr4z/VNJ3KA/pjQDuyutYJmkf4E7gWOCSumPNodS9ui0iQtJNwAWVChkHAad19oFKejfwSuYYfxm4PYPml3Jl/IOZ230AJZcaSUMyx3gAZVX/shzfFFBEvCzp08DKfECxIQfIzekVOmk33dNn9Hbu4fxm5IenzMyaVn0O8o0RMb66Q0TcIOmFytAHgIm58jkA+DXwi7o5rZRqDNSNPyDpUkrubVBWdL+cmzcDLsngcSWwkPb86dOBc4H5kpZRHmabAjyT+cAnUx5aGwhMjogFOW8CMF3SOOBpSu4uEbFA0nRK8LkSOClXrwFOBK6kZF7OpP0hxcuBq/OBvsWUQJyIWJyr7HfnfudUHtj7GvBvlAocD0i6ISK+DHwYuErSqryGcZWP6Z8pv0RsSPml5LgcP0bSSfn6OuCKfD0EuClX8v8EfLH+c6+ncLHNptPS0vI6sMHcuXM7/JvswhPH9OiHPnzi1Lcc97WVXeYSmZmZ9TobD2r8ULv1X15Bbk6vAhs03PqZd+5C+rvu1rw1MzOz3sMBcnPaqNOtW3W61dYiB8e2rq1qCwb6AT4zs7XKAXJzWgq8t9HGZ9/Xs4M3Sx1ks2bQ5gDZzGytc4DcnJ6iJMd36P+e0bMyb59wEQtbD7xS2rENBvnhUDOztc0BcnPai06awBz5Hpd5s77HwbGZrQ9ZRWFeZWhqREyQNBvYLCJacr8W4OKI2C/Liv0npUueKDWMR0XE8qxM8ZOI+GLOGwQ8C9wZEYdJOhtYHhEXV67hSaAlIhZJOp1Som0V0AacEBF35nHOoVSheDmn/jwizs9jjKJUzRgI/DgiJuT4lsA0SuOSJ4HPR8SS3HYapXrEKuBrEXFTju9FexWLGyid7ELSRsBVlDjkReDoiHgy59xIqRX9u4g4rHJvO1ACiy2Be4EvRsTrWRJuMqUiyGvAlyJifhf38hFKabfN8l7+PiJekvT3wDcrP8PdgT2zoUuHHCA3p9cohbQ7dAY9W0F2eGxmZv3IqxGxR4NtQyQdEhEz68ZPAZ6LiN0Asn7xG7ntZWBXSZtExKvApymlx7okaV/gMEpwt0LSe2mvdXwepVTablkHeTDw9Zw3EPhhnquV0tlvRtYCHg/cmkH/+Hx/qqSdKWXadqHUQb5F0k5Z6m0ipbzcHZQAeRSl1Ns4YElEDJc0BrgQODqv7yJKbHJC3W1dCHw3IqZKuiyPMRH4/4D7IuJwSR/K6z+gi3v5MfCNiPiNpC9RguJvRcQ1wDX5WewGXN9ZcAwOkJtVK6XmX4fOwyvIVa40YetDW1u4O55Z33cRpSFFfYA8lJLuCEA2tKiaSakpdS2l3fTPgE9043xDgUURsSKPuwjebITxFWD7iHgtty0Dzs55I4GFEVFrZDIVGE2pLzya9i53UygNTU7N8al5rieytvHIXM3ePCLm5LGuojTZnZlzaue8ltK4RFHcWtcQpdYh71O0Ny2ZkvMnUtoufDvv5WFJ20vamtLCu9G9fJDSaAVgFqXu87fqPsPa590pB8jNaTs6aRTiMm+rc3Bs64ODY7PeQdLxtDfbAJgUEZMq7+sbhXw7Iqbl6znA4ZL2B5ZV9pkM3CzpSOBWYEql+x2UlaYzJf2K8uf+yXQvQL455z0K3AJMi4jfUJ6ffzqD4o4MA/5Yed8K7J2vt46IZwGy3fSQypw76uYMo6yEt3Ywvtp5sjnJUkrtrEUNrmsr4M/ZCrr+WPcDR1AapowE/pLSorqze5kPfJbSDfAoVm+VXXM0JaDulAPk5rRhp1t/3cOjH9b1LmZmtm75r19rRwbDkzrZpbMUCyipDWdQVl1rx7xP0o6UlsoHUtIA9o2Ih3L7A5K2p6xm3lB/SY0vNZZn/u8ngP2BaZkWcW91R0nHUdI8tgI+Bh02Q+mqwVejOZ0da03P09n+E4Dv5y8n84DfUzr6dTbnS8APJJ1JaXv9+monk/amtK+e38k1AQ6Q+6XHv96z+S7zZma2/jk47h0i4rZso7xP3fhySrvj67LF8aHAQ5VdZgAXU9Ibqh0KXqSkUlQNpjzoR+YAzwZmS5oHjAWmA9tJGhwRyyLiCuAKSfMpD7K1svpq6jbAM/n6OUlDc/V4KKW1NZ3Mac3XHR2rNqc1Hxrcgk6qalFWlt8taVCuIr95rIh4iWwhnakYT+TXpo3uJSIepvxSgqSdeOvfzMfQjfQKcIDcrF6jPFnaoR1/1MOjf6eH883MzJrL+ZTqCbW82I8DD0bEEkkbUvJpZ9fNmQwsjYh5dbm5twPXSJoQEcskHQHcHxGr8mG/tkq6xh7AUxHxiqTLKTm/J+RDegNp/4vy3cCIrBjxJ0qgWMv7nUEJsifk9+sr4z+V9B3KQ3ojgLvyOpZJ2ge4EzgWuKTuWHOAI4HbIqLhCnJWvviv3Hdq9fyS3k1Z7X0d+DJwe1akaHgvkoZExPOSBlBW9S+rnSvHjgL+ptH1VDlAbk4b0/GfIAB4/J96dnCvIJuZWT9Sn4N8Y0SMr+4QETdIeqEy9AFgYq58DqAkN/6ibk4rpVQZdeMPSLqUknsblBXdL+fmzYBLMnhcCSykPX/6dOBcYL6kZcCrlIfensl84JMpD60NBCZHxIKcNwGYLmkc8DQliCQiFkiaTnn4bSVwUq5eA5xIe5m3mbQ/pHg5cHU+0LeYErwCIOm3wIeAzSS1AuOybNypwFRJ51HSKC7PKR8Grsoyew9SqlvQxb0cI+mkfH0dcEXlo/0boLX2cF9X1Elgb31US0vL68AGc+fO7TBIvnThmB790E8ePvUtx31tZZe5TGa2lrl5ilnPbTyo8YKS9V9eQW5OnQaro27p4dG9hGzWKzg4NjNbNxwgN6fOe8/e/w5dhZmZmVkf5AC5Oa2kk5/t3a5iYWZmZtaQA+TmtBTYoNHG61f1rNX0MT2a3Tu5nqitD/7vzsysd3KA3JyeprNW01f3sNX0ec3RaroanKzNIOWNVW1sMLDzLBczcHBs1hdkFYV5laGpETFB0mxgs4hoyf1agIsjYr9s/fyflC55otQwHpWNPgL4SUR8MecNAp4F7oyIwySdDSyPiIsr1/Ak0BIRiySdTilrtgpoA06IiDvzOOdQqlC8nFN/HhHn5zFGUapmDAR+HBETcnxLYBqwPfAk8PmIWJLbTqNUj1gFfC2rTpDNSq6kVLG4ATglS7ZtBFwF7EWp53x0RDyZc8ZSSq8BnBcRU3L8cqAlP6dHgX/Mz0l5vYcCr+T4vTnnXymVPSJ/NsfVWmzn9m9Q2oC/r9KOe3fgP4DN83P7aHVOPf8r3oTmzp07ks4e1Numh19NYl0FJw6O7Z3Q1ubCMWbvkFcjYo/K14TKtiGSDulgzinAcxGxW0TsSgky38htLwO7Sqr1K/g0pZ5vlyTtS+lnu2dE7E7p0ldru3wepV7xbtn57xPkX5OzJvIPgUMoNZmPkbRzzhsP3BoRIyhtscfnnJ0pZdp2AUYBP8rjAEyklJcbkV+jcnwcsCQihgPfBS7MY20JnEVpCT0SOEvSe3LOv0bER/J+ngZOzvFDKsc/Ps+JpGHA1yi/MOxKCfir5eS2zc/06crYIOAnwFcjYhdKc5baz6ND/pe8eTUO/7bq4ZeZrXcDXMHCepF+/AvbRbSvilYNpRL0RsQjEbGisn0m7V3ejqGb3d3yuItqx4qIRRHxTK5YfwX459qqaHbUOzvnjQQWRsTj2XhjKjA6t42m1Esmv3+uMj41IlZExBOUmssjs9ve5hExJ5uAXFU3p3asa4EDciX4YGBWRCzO1elZZFCdHfNq3fI2oX2BbzRwVRR3UDru1ToMDqLUpx5E6axX6+QHJTD/N1ZfKDwIeCAi7s9zvlip6dwhp1g0r8b/t7qth0c+qofzzcysqTT5L2z1jUK+HRHT8vUc4HBJ+wPLKvtMBm6WdCRlVXZKpfsdlAD1TEm/oqRhTKas+Hbl5pz3KHALMC0ifkN5fv7piFjWYN4w2leaobSE3jtfbx0RzwJku+khlTl31M0ZRll5be1gfLXzZEOPpZSltY7OX5uDpCsoqRQPAl+vP1Z1TkTMlXQxZYX4VeDmiLg5j/NZ4E8Rcb9W/zPxTkBIugl4HyXw/18dfE5v8gpy81q5vi+gr3CvHHs7+vGKmVlTkXS8pLmVr+PrdqlPsZhWt/086laRI+I+YEfKCvOWwN2SPlzZ/gAl5/cYSg7vatMbXGpExHJKfu/xwAvANEn/2ME9HSfpPkl/zJSDjn6D6ep/Yo3mdHastzOHiDiOkh7yEHB0Z8fK1IzRwA45512S/iFX0U8Hzuxg3iDgr4G/z++HSzqgg/1Wm2DNqfHPtonyiNcGPyhlb0eTr5iZ9RsRMQmY1IP5t0k6F9inbnw5pd3xdZLaKCukD1V2mQFcTMmHrSYwvkhJpagaTHnQj0wNmA3MljQPGAtMB7aTNDhTK64ArpA0n5Kj2wpsWzneNrSnJTwnaWiuHg+ltLamkzmtrB5JVI9Vm9Oa6Q9bUFpOt+Z9VufMrt5gRKySNA34JqVFdKPzHwg8EREvAEi6DvgYpcvDDkBt9Xgb4F5JI/NYv6k8sHcDsCdldb9DXkHuj1p7+GVmZmZV51PyXgGQ9PHaQ2iSNqQ8GPdU3ZzJwDkRMa9u/Hbgs5IG5/wjgPszgPygpBGVffcAnoqIV4DLgUslbZzzBgIb5n53AyMk7ZDXM4YSoJPfx+brscD1lfExkjaStAPlYbm7Mh1jmaR9Mm/42Lo5tWMdCdyWeco3AQdJek9+LgcBN6kYntcr4G+BhyvHOjb32QdYmud+GthH0qY55wDgoYiYFxFDImL7iNieErHsGRH/L8+/e84ZBHySks7RkFeQ+6OPrO8LMHv7XDvYzN5h9TnIN0bE+OoOEXGDpBcqQx8AJmYANwD4NfCLujmtlDJm1I0/IOlS4HdZEu55SkkzgM2ASyS9m5JKuZCSbgElveBcYL6kZZT83CnAM5kPfDIlUBwITI6IBTlvAjBd0jhK8HlUXscCSdMpgeRK4KTKg20n0l7mbWZ+QQnSr5a0kLJyPCaPtThX2e/O/c7JsQHAFEmbU1Iq7s9jQ0k9OTTv8RXguDzWnZKuBe7N6/o9XfwFICKWSPpOnj+AGyLi153NUTgBsym1tLS0zZ07t8O/ECy8b0yPfujD95j6lvDktZVd5jKZ2VrU1hZO8zBbCzYe1EnVJ+u3vILcvBqXL/mPHh55Yg/nm1mPOTg2M1t3HCA3r4b55Wd8ffMeHbg5+uiZmZmZdcwBcj903r+/1LMDeAXZ7B3n3Gszs3eOq1g0r4Xr+wLMbO1xcGxm9s7xCnLz2qHhlk0abrEm1xbBAEda64xXec3MmoNXkJvXwIZbtunhl/VZDo7XrXfy43UnP7N3hqRV2ZWu9jU+x2dLmlvZr0XS7Hy9qaRrJM2TNF/S7yRtlttC0tWVeYMkvZBtp5F0tqRv1F3Dk5Lem69Pl7RA0gN5PXtXjnOBpMcq13p65RijJD0iaWHtHnJ8S0mzct6sWv3m3HZa7v+IpIMr43vlvS2U9IMsZ0fWTJ6W43dK2r4yZ2ye4zFJYyvj1+Tx50uaLGmDHN9C0v+RdH/e73F1n8lASb+vfW6d3Yuk7SW9WvlcLuvq5+4V5Ob1eqMNd3+2Zwce3rPpth55BXn9W1urzK5iYfaOeTUi9miwbYikQyJiZt34KcBzEbEbgKQPAm/ktpeBXSVtEhGvAp8G/tSdC5G0L3AYpQHGigyaa81AzgPeD+wWEa+pNBr5es4bCPwwz9VKaX09IyIeBMYDt0bEhAycxwOnStqZUsd4F0pL51sk7ZS1kCdS6i/fQalXPIpSC3kcsCQihksaA1wIHC1pS+AsoIVSh/iePP8S4BrgH/Iefkqp+TwROAl4MCL+VtL7gEckXRMRtfjmFEpnwmrlgQ7vJbf9oZOf41s4QG5eDf/1/OgLjbZ0kyPkPsvB8frnH4FZU7kIOIP2Rhk1Q6l0zouIR+q2zwQ+A1wLHAP8DPhEN843FFgUESvyuLXWyZsCXwG2j4jXctsy4OycNxJYGBGP5/5TgdGUJiCjaW8DPYXSAvrUHJ+a53oim3+MlPQksHlEzMljXQV8Lu9pdOWc11I6+wk4GJgVEYtzzixKUP2ziLihdnOS7qL9b9UBDM75m1Eaj6zM/bbJz+984H9UPp9G97LGHCA3r1cabvl5D4+8bw/nm5mZ9RKSjqe9Gx3ApIiodmar76T37YiYlq/nAIdL2h9YVtlnMnCzpCOBW4EpEfFYZftU4MxMD9g99+9OgHxzznsUuAWYFhG/oSxdPZ1BcUeGAX+svG8F9s7XW2cLZyLiWUlDKnPuqJszjLIS3trB+Grnye59S4GtGpx/WOU9mVrxRcrKMMCllHbTzwCDgaMjoi23fY/S2ntw3X02uheAHST9HngJOCMifksnHCA3ry0abbj9n3p24GZcQPbDVWZm/VMGw521Ku4sxQJKasMZVFYqI+I+STsCBwEHUlIa9o2Ih3L7A5mfewwlRWG1S2p8qbFc0l6UYHp/YFqmEtxb3THzdU+hBKcfo+O/Knf1IEOjOZ0d6+3MqfkRcHslcD0YuA/4FKV19yxJvwX+Bng+Iu6RtF8n11/1LLBdRLyYn9//lrRLRDSse+sAuXktb7TBz9m9lYNje6esagsGOn/YrGlExG2SzgX2qRtfDlwHXCepDTiUkjNbMwO4mJISsFVl/EVKKkXVYODPedxVlNSB2ZLmAWOB6cB2kgZHxLKIuAK4QtJ8ykP7rcC2leNtQ1mZBXhO0tBccR0KPJ/jjea0snooUT1WbU6rpEGUxbrFOb5f3ZzZtTeSzgLeB5xQ2ec4YEJEBLBQ0hPAh4CPA5+VdCiwMbC5pJ9ExD80updME6mlpdwj6Q/ATsBcGnCA3Lze1WjDjlf28Mjn9XB+P/HGqjY2GOhCMbY6B8dmTel84DKgluP7ccoDZkskbQjsTCUgTJOBpRExr24l9HbgGkkTImKZpCOA+yNiVT7s11ZJ19gDeCoiXpF0OSXn94R8SG8g7Q/w3Q2MkLQD5YHAMcAXctsMSpA9Ib9fXxn/qaTvUB7SGwHcldexTNI+wJ3AscAldceaAxwJ3BYRIekm4IJKhYyDgNPys/oyZbX4gEoKBcDTwAHAbyVtDXwQeDwiTqvM3Q/4RgbHDe8lH/JbnNe+Y97L43TCAXLzavhngxv/sWcHPrln0/uN3hocO52k9/PPyKxXqc9BvjEixld3iIgbJFUfgf8AMDEfMBsA/Br4Rd2cVuD79SfL9ItLgd9JCsoq6Jdz82bAJZLeTXlgbSHt+dOnA+cC8yUtA16lPKj2TOYDnwzcRFlRnhwRC3LeBGC6pHGUoPSovI4FkqZTHuRbCZyUq9cAJwJXUjorzKT9IcXLgavzgb7FlECciFicq+x3537n1B7Yo/xi8RQwp3xcXBcR5+S9XJmr5AJOrT2U2IkO74WSlnGOpJXAKuCrlfN3SGXl2ppNS0vLU3Pnzv3LjrYtPHFMj37owydOfcs/3a+t7DKXqU9wYGJm1r9sPKhx1Sfrv3rnEpetDX+xvi+gL3JwbGZmZk6xaF4Nf/lZ+vV38jLMzMzM+hYHyM3rtUYbtpjRwyP/j653MbO3zx0PzczWLwfIzWtVow1Le9hq2szWLQfHZmbrlwPk5tWwzNsWt/TwyM3YKaSf8MqkmdmakbQKmFcZmhoREyTNBjaLiJbcrwW4OCL2y9bP/0npkidKDeNR2egjgJ9ExBdz3iBKI4s7I+IwSWcDyyPi4so1PAm0RMQiSadTSrStAtqAEyLizjzOOZTKDS/n1J9HxPl5jFGUqhkDgR9HxIQc3xKYBmwPPAl8PiKW5LbTgHF5rq9FxE05vhftVSxuAE7Jcm4bAVcBe1HqOR8dEU/mnLGUhioA50XElBy/EvgksDS3/WM2Wvkm8Pc5Ngj4MKVW8vvyemt2BM6MiO9lpYzR+bk8n8d6Jrv0/RjYM491VUR8m044QO6HbjywZ/Nd5q3vcnBsZrbGOuukN0TSIRExs278FOC5iNgNIOsXv5HbXgZ2lbRJRLwKfJpSm7hLkvYFDgP2jIgVkt5Le63j84D3A7tlHeTBwNdz3kDgh3muVkpnvxkR8SAwHrg1g/7x+f5USTtTyrTtQnnw/xZJO2Wpt4mU8nJ3UALkUZRSb+OAJRExXNIY4ELg6AzCzwJaKB307snzL8lr/2ZEXFu914i4CLgor/9vgX/N0myLKfWfa/f1J+CXOe2iiPhWbvsacCbwVcovDRtFxG75y8uDkn5WC9474gC5eTWs7zeqYY89MzMzWwMXUVZF6wPkoZTavgBExCN122cCnwGupbSb/hmlfXRXhgKLsjMctbrAGfR9Bdg+Il7LbcuAs3PeSGBhRNQamUylrLQ+mN/3y/2mUBqanJrjU/NcT2Rt45G5mr15RMzJY10FfC7vaXTlnNdSGpeI0ghkVq32sKRZlKD6Z924Z2j/jOodAPwhIp7Ke672gHgX7e2sA3hXrrJvArxOJ/0iwAFyM2uYg8wdPTzyHj2cb9YHtbUFA9wFz6zpSDqe9mYbAJMiYlLlfX2jkG9HRO1P/HOAwyXtDyyr7DMZuFnSkcCtwJRK9zuAqcCZkn5FScOYTPcC5Jtz3qPALcC0iPgNJfnx6QyKOzIM+GPlfSuwd77eOiKeBcgWzUMqc+6omzOMshLe2sH4aufJ5iRLKW20Ozr/sMr78yWdSfmsxtd+AYA3g/9RdPwH7DHUBc6Szqd091sK7J/D11KC92eBTWlfjW7IAXLzel/DLds03GJmDTg4NmtOGQxP6mSXzlIsoKQ2nEFZda0d875saXwQcCAlpWHfiHgotz8gaXvKyugN9ZfU+FJjeeb/foIS/E3LtIh7qztKOo6S5rEV8DHosBlKVw2+Gs3p7FhvZ85pwP+jpIpMonyO51T2+1vg/9YHtNnC+7M5v/2gEacDp2f+9MmU1I6RlIXDvwDeQ2lffUttRb0jbhTSvBqWeePVHn6ZmZkZABFxG7AxsE/d+PKIuC4i/gn4CXBo3dQZwMW8NXXgRUoQVzWY8qAfEbEqImZHxFmUAPDvKC2nt8u8YyLiigzql1IeymsFtq0cbxvgmXz9nKShAPn9+RxvNKeV1Zfaqsd6c06mM2xBSflseP6IeDaKFcAVlGC26i2rxOkQ4N6IeK6DbQA/pXw2UB5qvDEi3oiI54H/S8mHbsgBcvPasOGWR3v4ZWZmZlXnA/9WeyPp45Lek683BHamkpOcJgPnRMS8uvHbgc/Wgl1JRwD3R8QqSR+UNKKy7x7AUxHxCnA5Jed345w3kPZY4G5ghKQd8nrGUAJ08vvYfD0WuL4yPkbSRpJ2AEYAd2U6xjJJ+2R+8bF1c2rHOhK4LSICuAk4SNJ78nM5KMeoBOei5DLPr3yOW1AqXNSOX/WWvOS6z+azwMP5+mngUyreRfll5mE64RSL5tXwTyd3H92zA7vKm5mZ9SP1Ocg3RsT46g4RcYOkFypDHwAmZtA3APg18Iu6Oa2UsmvUjT8g6VLgd1kS7nngy7l5M+ASSe8GVlJWjmv506cD5wLzJS2j/M13CvBM5gOfTAlKBwKTI2JBzpsATJc0jhJIHpXXsUDSdMqDfCuBk7KCBcCJtJd5m0n7Q4qXA1fnA32LKYE4EbE4S7DdnfudU0mZuEbS+yhpGPdRqk7UHA7cHBEvV8ZqecmfBk6o+/gmZMWQNsovJLVj/ZCyOj0/z3NFRDxAJ1QCe2s2LS0tL82dO3fzjrYtvGxMj37ow7869S25RK+t7DKXycw6EAGuvme2/mw8qMP8WOvnvILcvDZotMF1kM16DwfH9nb5lyuzdccBcvNqmIM86oVGW7rJORZm7ziXmbN6Do7N1h0HyM2rtdGGZxsXgOsWx8d906q2YKADrD6rPjh2wGxmtu64ikXzev/6vgDrXRwcNxcHx2Zm644D5OY1v9GGoS/07KtZrGrzc4VmZtY5Sask3Vf5Gp/jsyXNrezXIml2vt5U0jWS5kmaL+l3kjbLbSHp6sq8QZJeyK56SDpb0jfqruFJSe/N16dLWiDpgbyevSvHuUDSY5VrPb1yjFGSHpG0sHYPOb6lpFk5b1atPF1uOy33f0TSwZXxvfLeFkr6QVbrIEvCTcvxO7MZSm3O2DzHY5LGVsYl6XxJj0p6SNLXKuM/yGM9IGnPHP9g3c/jJUn/ktumVcafrFUfkbSVpP+StDwrhHTJKRbN6yMNt2zyDl5FL+YVVTMz64bOOukNkXRIRMysGz8FeC4idoMS1FFaNAO8DOwqaZOIeJVSruxP3bkQSfsChwF7RsSKDJprzxydR/nr8W4R8ZpKHeWv57yBlFJnn6akYN4taUZEPAiMB26NiAkZOI8HTpW0M6VM2y6UDnS3SNopS71NpJSXu4PSCXAUpdTbOGBJRAyXNAa4EDha0paUjnYtlDK09+T5lwD/SGki8qGIaFN7q+tDKLWXR1DaYk8E9o6IRyj1n2v39SfglwAR8WYhW0n/TmmUAqV52reAXfOrSw6Qm1fD6O/uzXp2YOcgm5mZAXARpc10fYA8lEpjkAzqqmYCnwGupb3hxSe6cb6hwKLsOkdELII36wJ/Bdg+Il7LbcuAs3PeSGBhrbWypKnAaEqN49HAfrnfFGA2pd3zaGBqnuuJrG08UtKTwOYRMSePdRWlwcfMnFM757WUxiUCDgZm1WofS5pFCap/Rqmp/IWIaMvrrnXyGw1clY1G7pD0bklDs1FJzQHAHyJitSYsec7PA5/KY75MqSvd7RDGAXI/1Hhp2czMrH+RdDztzTYAJkXEpMr7+kYh346Iafl6DnC4pP2BZZV9JgM3SzoSuBWYEhGPVbZPBc7MtIrdc//uBMg357xHgVuAaRHxG8ra1dMZFHdkGPDHyvtWyqoswNa1oDMinq2s4A6jrBBX5wyjrIS3djC+2nmyOclSYKsG56/N+QBllflw4AXga/lZNZpTDZAbtaH+BGUF/7EOtnWLA+Tm9XKjDa++k1dh1iRcNcKsOWUwPKmTXTpLsYCS2nAGZdW1dsz7JO1Iaal8ICWlYd+IeCi3P5D5ucdQUhRWu6TGlxrLJe1FCQD3B6ZlWsS91R0lHUdJ89gK+Bgd/1W5qwdxGs3p7FhvZ85GwGsR0aLSVrv2y0Kn16zSMvuzwGkd7PeWNtRrygFy82pY5m2Lf+/hkSf2cL5ZH+Tg2Mw6EhG3qbRR3qdufDlwHXCdpDbgUOChyi4zgIsp6Q1bVcZfpKRSVA0G/pzHXUVJg5gtaR4wFpgObCdpcEQsi4grgCskzae0lm6l5PnWbAM8k6+fq6UuSBpKaW1NJ3Na83VHx6rNaZU0CNiC0nK6lfY0jtqc2ZU5tTbcv6S0hO7s/DWHAPdGxHOVMfK8RwB70QMOkJvXjo02nPH17vwVp7GpPZptZmbWdM4HLgNqOb4fBx6MiCW50rkz7QFhzWRgaUTMk7RfZfx24BpJEyJiWa6q3h8Rq/Jhv7ZK6sAewFMR8Yqkyyk5vyfkQ3oDaX+A725ghKQdKA+1jQG+kNtmUILsCfn9+sr4TyV9h/KQ3gjgrryOZZL2Ae4EjgUuqTvWHOBI4LaICEk3ARdUKmQcRPvK7/+m5ApPBj4JPFo51smZL713flbV9IpGq8QHAg9HRMOFwu5wgNy8Graa/hK/7eGhT+rhfDMzsz6jPgf5xogYX90hIm6QVC2E+gFgYj4sNgD4Ne2rpLU5rcD360+W6ReXUh4qC8qK7pdz82bAJZLeDawEFtKeP306cC4wX9IySkblFOCZzAc+GbiJsqI8OSIW5LwJwHRJ44CngaPyOhZImk55kG8lcFKuXkN5sO5KSl2smbQ/pHg5cHU+0LeYEogTEYtzlf3u3O+c2gN7ef5rJP0rsLxyrzdQVt0XAq8Ax9U+o3wo8dPACfWfHw3ykmsPFwIbSvoccFBW8eiQysOB1mxaWlremDt3bodB8sKfj+nRD334UVPf8rfm11Z2mctkZmbW62w8qHHVJ+u/vILcvBo+pHf1L87r0YH/51E9mm5mZmbWqzlAbl4NuyR+cfwZPTy0s5DN1rUIkNe1zMzWCwfIzathDvJqVQ3fjj16OL/JtUUwwJGN9ZD/EzIzW38cIDevDRtuuf8dvIp+yMGx9ZR/yTIzW78a/hnemtirPfwys3XKwbFZ7yFplaT7Kl/jc3y2pLmV/Vokzc7Xm0q6RtI8SfMl/U7SZrktJF1dmTdI0gvZVQ9JZ0v6Rt01PCnpvfn6dEkLJD2Q17N35TgXSHqscq2nV44xStIjkhbW7iHHt5Q0K+fNqpRiQ9Jpuf8jkg6ujO+V97ZQ0g+yWgeSNpI0LcfvzGYotTlj8xyPSRrbwed8iaTllfejK/c4V9Jf1+0/UNLva59b5bP7U+X+D61s213SnPzs5knauKOfd41XkPujfbrepT9wjqeZmXVDZ530hkg6JCJm1o2fQml1vBtA1i9+I7e9DOwqaZOIeJVSruxP3bkQSfsChwF7RsSKDJprfzE+D3g/sFvWQR4MfD3nDQR+mOdqpXT2m5FlzsYDt0bEhAycxwOnStqZUjJtF0od5Fsk7ZSl3iZSysvdQSnHNopS6m0csCQihksaA1xIaSO9JXAW0ELphndPnn9JXl8L8O66270VmJF1lHenNEP5UN1n/BCldFvVdyPi4rrPbRDwE+CLEXG/pK1o/3l0yCvIzaut4ZYXe/jVJBwcW0+0tbmyoZlxEaXNdL2hVILeiHgkIlZUts8EPpOv16Qt8lBgUe1YEbEoIp7JusBfAf45Il7Lbcsi4uycNxJYGBGPR8TrlKftR+e20ZR6yeT3z1XGp0bEioh4glKPeKRKt73NI2JOlFrBV9XNqR3rWuCAXF0+GJgVEYszKJ5FCaprwftFwL9VbzQilkd7LeJ3sXqb6W3y8/txNz+3g4AHIuL+PPaLlZrOHXKA3LyWNdzykR5+mZlbT5s1CUnH55/wa1/H1+2ySV2KxdGVbXOAFZL2r5szmbIKO0fSeZJG1G2fCozJP/PvTulI1x03A9tKelTSjyR9MseHA09HRKN/+4cBf6y8b80xgK1rHery+5Au5gzL1x0d6805EbESWEppo93Z+U+mrBRXu+QBIOlwSQ9TGq18qbLpe5SAuqPFwJMzNWNyJV1kJyAk3STpXkn/1sG81TjFoj/6dQ/nH7ZWrqJXctqFmVn/EhGTgEmd7NJZigWU1IYzgFMrx7xP0o6UlcsDKSkN+0bEQ7n9gczPPYaSorDaJTW+1FguaS/gE8D+wLRMi7i3uqOk4ygpCFsBH4MOm6F09WewRnM6O9YazZH0F5TOfft1dAER8Uvgl5L+htIl8EBJhwHPR8Q9Wr1FN5TUj3PznOcC/04JrAcBfw18lNKV71ZJ90TErR2dFxwgN7PBjTYs/fo7eRl9i4Nj6ytc6cKsd4iI21TaKO9TN74cuA64TlIbpW3yQ5VdZgAXU4LDrSrjL1JSKaoGA3/O464CZgOzJc0DxlLyc7eTNDhTK64ArpA0n9JauhXYtnK8bYBn8vVzkoZGxLOZPvF8jjea05qvOzpWbU5r5v1uQWk53crqQfA2eQ9/RVn9XpjP+W0qaWFEDK/efETcLukDmXP9ceCz+QDexsDmkn4SEf8QEc/V5kj6T6D2AF8r8JuIWJTbbgD2pOQ5d8gBcvNq+JvhFrf08MjDu97FzNYtB8dmvcr5wGXA4wCSPg48GBFLJG0I7EwJCKsmA0sjYl7dSujtwDWSJkTEMklHAPdHxKp82K8tIh7LffcAnoqIVyRdDlwq6YR8SG8g7Q/w3Q2MkLQDJTd6DPCF3DaDEmRPyO/XV8Z/Kuk7lIf0RgB35XUsk7QPJTXkWOCSumPNAY4EbsuH7G4CLqikPBwEnBYRiykPFpKf2/JacCxpOPCHnL9n3suLEXEacFrusx/wjYj4h3w/tJKqcTgwP1/fBPxb5mq/DnwS+C6dcIDcvF5rtOGMA0/q0YHdR8/MzPqRTSTdV3l/Y0SMr+4QETdIeqEy9AFgYj6gNoCS3PiLujmtwPfrT5bpF5cCv5MUlBXdL+fmzYBLJL0bWEl5cK6WM306Ja1gvqRllMKsU4BnImKlpJMpgeJAYHJELMh5E4DpksYBT1NSHoiIBZKmAw/muU6qPNh2InAlsAnlgcNaFY/LgaslLaSsHI/JYy3OVfa7c79zMjjuzN8Bx0p6I+/l6MpDe438L0l7UBYJnwROyPMvyUD/7tx2Q0R0mnCqrs9lfVFLS8vrc+fO7bBZyMLLxvTohz78q1PfsnT12souc5nMzMx6nY0HdZgfa/2cV5Cb1/KGW9xJz6wptLWFq2mYma0DDpCb1yuNNtzdw4f0nIJs1js4OO4d/MCkWfNxgNy8nm604aP/3sMjT+zhfDOzJuLg2Kz5uFFI8/qr9X0BZmZmZn2RA+Tm1WmPcTMzM+uapFV1nfTG5/hsSXMr+7VImp2vN5V0jaR5kuZL+p2kzXJbSLq6Mm+QpBck/Srfny3pG3XX8GTWAEbS6ZIWZLe4+yTtXTnOBZIeq1zr6ZVjjJL0iKSFtXvI8S0lzcp5syql2JB0Wu7/iKSDK+N75b0tlPSDrNaBpI0kTcvxO7MZSm3OjZL+XLvPyvgOue9jOXfDuu0fzZ/Bkd24l4+odC+cJ+n/SNo8x0dWPpP7JR3e2c8cnGLRzBqWT3ndjUIacic9MzOr01knvSGSDomImXXjpwDPRcRuAFm/uLZw9TKwq6RNIuJV4NOU2sRdkrQvpZ/tnhGxIoPmWkB5HqWm8G5ZB3kw8PWcNxD4YZ6rldLZb0ZEPAiMB26NiAkZbI6ntMnemVKmbRdKHeRbJO2Upd4mUsrL3UHpBDiKUuptHLAkIoZLGgNcCNRac18EbEqWXqu4EPhuREyVdFkeY2Llui+klKejMtboXn5MqYv8G0lfAr4JfItSD7kly90NBe6X9H+yHXaHHCA3r2GNNpzD1j06cDPXQXZwbH2BHwoz6zUuorSZrg+QhwJP1d5ExCN122cCnwGupbSb/hmlfXRXhgKLImJFHrfWGW5T4CvA9hHxWm5bBpyd80YCCyOi1shkKjCaUuN4NO1d7qZQGpqcmuNT81xPZG3jkZKeBDaPiDl5rKuAz+U9ja6c81pK4xJFcavqWkPnyvOnaG9aMiXn1552+mdK/eiPVqZ1di8fpDRaAZhFCay/FRHVwgUb03WbbQfITez1RhvOu+W5Rpu6x2UszNYrB8dma4+k42lvtgEwKSImVd7XNwr5dkRMy9dzgMMl7Q8sq+wzGbg50wJuBaZUut9BWWs6M9MNds/9uxMg35zzHgVuAaZFxG8o/zI/nUFxR4YBf6y8bwX2ztdb17rPZbvpIZU5d9TNGUZZCW/tYHy18+Rq7VJKG+1FDa5rK+DPlZXcN48laRilG96nWD1A7uxe5gOfpXQDPIpKq+xMRZkM/CXwxc5Wj8EBcr9094E9m+/42MzMmkUGw5M62aWzFAsoqQ1nUFZda8e8T9KOlJbKB1LSAPaNiIdy+wOZn3sMJUVhtUtqfKmxXNJelGB6f2BapkXcW91R0nGUNI+tgI9Bh81QulpFbTSns2Ot6Xk62/97wKnZ2rq7c74E/EDSmZS2128uFkbEncAukj4MTJE0s7ba3hEHyM3riUYbPnpLD4/sCNlsrXHeu1nfFhG3qbRR3qdufDlwHXCdpDbgUOChyi4zgIsp6Q1bVcZfpKRSVA0G/pzHXUVJg5gtaR4wFpgObCdpcEQsi4grgCskzae0lm6lspoKbAM8k6+fkzQ0V4+HUlpb08mc1nzd0bFqc1olDQK2oJNnoigry++WNChXdKvHagGmZnD8XuBQSSs7u5eIeJjySwmSdqKksawmIh6S9DKwKzC3fnuNq1g0r4YBMh/p4ZeZrTUOjs2awvnAv9XeSPp4rRpEVmXYmUpOcpoMnBMR8+rGbwc+mw/ZIekI4P5cSf2gpBGVffcAnsoc28spOb8b57yBtD/AdzcwIitGbEh5+G5GbptBCbLJ79dXxsdkZYodgBHAXZmOsUzSPplDfGzdnNqxjgRui4iGK8i57b9y39XOHxE7RMT2EbE9JZ/5nyLif3d2L7X0EEkDKKv6l+X7HTJgR9JfUnKVn2x0XeAV5GbW+Em8F9/BqzAzM+vb6nOQb4yI8dUdIuIGSS9Uhj4ATMwAcgDwa8rDZtU5rcD360+W6ReXAr+TFJQV3S/n5s2ASyS9G1gJLKQ9f/p04FxgvqRlwKuUh96eyXzgkykPrQ0EJkfEgpw3AZguaRylydhReR0LJE2nPPy2EjgpV68BTgSuBDahPJxXe0jxcuDqfKBvMSV4BUDSb4EPAZtJagXGRcRNlNSUqZLOA36fx2ioi3s5RtJJ+fo64Ip8/dfAeElvAG2UYLtRXnS53k4Ce2tSC38+pkc/9OFHTX3LmtdrK7t+ItTMzKy32XhQhzmt1s95BbkfOuOvNujR/GYu82ZmZmbmALkf2tlN9szMzMwacoDcD33h4R4ewFUszMzMrIk5QO6Hfvyhns2fsHYuw8zeJpeGMzNbt1zmrR86todfZrZ+OTg2e+dIWiXpvsrX+ByfLWluZb8WSbPz9aaSrpE0T9J8Sb+TtFluC0lXV+YNkvRCdtVD0tmSvlF3DU9Kem++Pl3SAkkP5PXsXTnOBZIeq1zr6ZVjjJL0iKSFtXvI8S0lzcp5s2rl6XLbabn/I5IOrozvlfe2UNIPsloHWRJuWo7fmc1QanPG5jkekzS2Mn5NHn++pMmSNsjxD0maI2lFB5/HKbn/Akn/UhnfQ9Idee9zJY3M8a0k/Zek5VkhpEsOkM3MeiEXGDLrNV6NiD0qX9U/pA6RdEgHc04BnouI3SJiV2AcvPkA0MvArpI2yfefBv7UnQuRtC9wGLBnROxO6dJXa7t8HvAXwG7Z+e8TQC3YHAj8EDiEUpP5GEk757zxwK0RMYLSFrv2C8DOlDJtuwCjgB/lcQAmUsrLjcivUTk+DlgSEcOB7wIX5rG2BM6itIQeCZxVCcSvoZR/241SNq5W0m4x8DVKM5XqZ7Ar8JU8zkeAw9ReG/p/Af8z7//MfA/wGvAtYLVAuzNOseiHNnQnPbNez6vEZn3CRZSGFDPrxodSaQwSEY/UbZ9J6fJ2LaXd9M8oAW1XhgKLImJFHncRlBVrStC4fa19ckQsA87OeSOBhRHxeO4/FRhNqXE8mtLND0rd5NmU2sSjgal5rieytvFISU8Cm0fEnDzWVcDn8p5GV855LaVxiYCDgVkRsTjnzKIE1T+LiDdbbUu6i+zSFxHPA89Lqu+G92HgjmyOgqTfAIdTguEANs/9tqC9w97LlLrS3Y5gHCD3R/t0vYuZ9S5tEQxw1Gy21kk6nvZmGwCTImJS5X19o5BvR8S0fD0HOFzS/sCyyj6TgZslHUlZlZ0SEY9Vtk8Fzsy0it1z/+4EyDfnvEeBW4BpEfEbytLV0xkUd2QY7SvNUNo1752vt87ueGS76SGVOXfUzRlGWQlv7WB8tfNkQ4+llDbaHZ1/WOU9mVrxRcrqe2fmA+dL2orSDOVQ2ltG/wtwk6SLKVkSH+viWA05QO6Pft3D+XusjYswszXh4Nhs3chgeFInu7yaf7Jv5DzKKvKplWPeJ2lH4CBKGsTdkvaNiIdy+wOZn3sMcEPd8RolWEVELJe0FyWY3h+YlvnE91Z3lHQcJdDcihIkdvQ/kK4SuRrN6exYb2dOzY+A2yPit51dVEQ8JOlCYBawHLif0ukPSoe/f42IX0j6PKUr34GdHa8RB8j90I1H92z+yWvnMszMzPq8iLhN0rnU/X02IpZT2h1fJ6mNstL5UGWXGZT82v0ogWzNi5RUiqrBwJ/zuKsoaRCzJc0DxgLTge0kDY6IZRFxBXCFpPmUdsytwLaV421Dph8Az0kamqvHQymtrelkTmu+7uhYtTmtkgZR0hwW5/h+dXNm195IOgt4H3AC3RARl5MtqSVdQPuK9ljaV6B/Dvy4O8friAPkfuhT6/sCbL3xn+nNzNaJ84HLgFqO78eBByNiiaQNKQ/Gza6bMxlYGhHzJO1XGb8duEbShIhYJukI4P6IWCXpg0BbJV1jD+CpiHhF0uWUnN8TIuK1fKBuw9zvbmCEpB0oDwSOAb6Q22ZQAssJ+f36yvhPJX2H8vDfCOCuvI5lkvYB7qQUuLqk7lhzgCOB2yIiJN0EXFB5MO8g4LT8rL5MyVE+ICLauvFZI2lIRDwvaTvgCGDf3PQM8EnKZ/0p4LGOj9A1B8j90IYv9PAAfkivz1KHf+UyM7NO1Ocg3xgR46s7RMQNkqr/un4AmJgPqA2gJDf+om5OK/D9+pNl+sWllIfKgrKiW6vssBlwiaR3U9IKFtKeP306cC4wX9IySn7uFOCZzAc+GbiJsqI8OSIW5LwJwHRJ44CngaPyOhZImk55kG8lcFKuXkNJZbiSUnViJu0PKV4OXJ0P9C2mBOJExOJcZb879zun9sAe5ReLp4A5WS3uuog4R9L7KbnFmwNtWc5t54h4CfhF5iC/kde1JI/1FeD7uXr9WuWzofZwIbChpM8BB0XEg/Wf/5v7h2sJ9TsLLxvTox/68K9OfUuU9drKLnOZ+g2v0pqZ9R0bD/LKgb2VV5D7o6263sXePq/SmpmZ9W0OkPuj23o4/6i1chVNy4vHZmZmfZsD5P5ok653MTMzM+uvHCD3R8eu7wswMzMz670GrO8LsHfe0s169mVmZtZfSFol6b7K1/gcny1pbmW/Fkmz8/Wmkq6RNE/SfEm/k7RZbgtJV1fmDZL0QnbVQ9LZkr5Rdw1PSnpvvj5d0gJJD+T17F05zgWSHqtc6+mVY4yS9IikhbV7yPEtJc3KebMqpdiQdFru/4ikgyvje+W9LZT0g6zWgaSNJE3L8TuzGUptztg8x2OSxlbGr8njz5c0OTvqIenv8x4fkPTfkj5SmTNZ0vNZ57n6OZ1b+VxulvQXOf5pSffkNd8jqcuKt15B7oe2eLiHB3CZNzMz6z8666Q3RNIhETGzbvwU4LmI2A0g6xe/kdteBnaVtElEvAp8mlKbuEuS9gUOA/aMiBUZNNdqHZ8HvB/YLesgDwa+nvMGAj/Mc7VSOvvNyDJn44FbI2JCBs7jgVMl7Uwp07YLpQ7yLZJ2ylJvEykl1O6gdAIcRSn1Ng5YEhHDJY0BLgSOlrQlcBbQQumgd0+efwlwDfAPeQ8/pZS0mwg8AXwya0kfQul2WGuPfSVwKXBV3Ud0UUR8K+/5a8CZwFeBRcDfRsQzknallLsbRie8gmxmZmb29lxEaTNdbyiVoDciHomIFZXtM4HP5OtjgJ9183xDgUW1Y0XEogz6NqXUAP7niHgtty2LiLNz3khgYUQ8HhGvA1OB0bltNKVeMvn9c5XxqRGxIiKeoNRcHqnSbW/ziJgTpVbwVXVzase6FjggV5cPBmZFxOIMimdRgmoi4oZIwF1kl76I+O9KfeM7qHTvi4jbKXWWV5M1kmveRbazjojfR0St298CYGNJG3X8ERdeQe6H7v5Qz+Z7AdnMzPqR+kYh346Iafl6DnC4pP2BZZV9JgM3SzoSuBWYUul+ByVAPTPTKnbP/T/RjWu5Oec9CtwCTIuI31D+aX46IpY1mDcM+GPlfSvtq7FbR8SzANluekhlzh11c4ZRVsJbOxhf7TzZnGQppbhsR+dfbQU3Uyu+SHur6KpxtDcj6ZSk8ylPWy0F9u9gl78Dfl/3C8tbOEDuhz56Sw8P4AjZzMz6gIiuS29KOp5KxzVgUkRMqrzvLMUCSmrDGcCp7eeN+yTtSGmpfCAlpWHfiHgotz+Q+bnHUFIUVrvshrcTsVzSXpRgen9gWqZF3Ft3T8dRAs2tgI9BhwX6u2rw1WhOZ8d6O3NqfgTcHhG/Xe0iyi8f44C/7vRqaweNOB04XdJpwMmU1I7asXahpH0c1NVxnGJhZmZmTak7dekjYlJEtFS+JnU9a7X5twEbA/vUjS+PiOsi4p+AnwCH1k2dAVzMW9MrXgTeUzc2GPhzHndVRMyOiLMoAeDfUdIftsu8YyLiigzql1JaS7cC21aOtw1QSzl4LtMmyO/P53ijOa1U0h3qjvXmHJV2z1tQUiE6Oz+SzgLeB/yP6k1L2h34MTA6Il5kzfyU8tnUjrUN8Evg2Ij4Q1eTvYLcDz1+YM/mewHZzMxsNecDlwGPA0j6OPBgPmC2IbAzMLtuzmRgaUTMk7RfZfx24BpJEyJimaQjgPsjYlU+7NdWSdfYA3gqIl6RdDlwqaQT8iG9gbQ/wHc3MELSDpTc6DHAF3LbDGAsMCG/X18Z/6mk71Ae0hsB3JXXsUzSPsCdlHSGS+qONQc4ErgtIkLSTcAFlQoZBwGn5Wf1ZUqO8gER0Vb7ECRtB1wHfDEiHu3ks3+TpBGVz+azwMM5/m7g18BpEfF/u3MsB8j90I7/3sMDTFwrl2FmZtYX1Ocg3xgR46s7RMQNkl6oDH0AmJgPqA2gBGe/qJvTCny//mSZfnEp8DtJQVnR/XJu3gy4JAO+lZSV41p6yOnAucB8ScuAVykPzD2T+cAnU6o3DAQmR8SCnDcBmC5pHPA02S83IhZImg48mOc6KStYAJxIqSSxCSU3uJYffDlwtaSFlJXjMXmsxZLOpQTqAOdERO0hu8uAp4A5WS3uuog4h1KBYivgRzm+MiJaACT9DNgPeK+kVuCsiLgcmFD7JSKP+dU8x8mU9b1vSfpWjh0UEbXV8rdQeWjQ+pOFJ47p0Q99+MSpb/mj1Wsru8xlMjMz63U2HtRhfqz1c15B7o+6LI9tZmZm1n85QO6PupXJY2ZmZtY/OUDuj7yCbGZmZtaQA+T+6Oc9nL/vWrkKMzMzs17JAXJ/9Or6vgAzMzOz3ssBcn90wvq+ADMzs75B0ipgXmVoakRMkDQb2KxSeqwFuDgi9pO0KfCflDbSojT5GJWd8AL4SUR8MecNAp4F7oyIwySdDSyPiIsr1/Ak0BIRiySdTqlhvIpSzuyEiLgzj3MOpUzbyzn15xFxfh5jFKWs3EDgxxExIce3BKYB2wNPAp+PiCW57TRKF7tVwNci4qYc34v2Mm83AKdkveONgKuAvSgNT46OiCdzzlhKx0GA8yJiSo5fA7RQWljflffzRm7bD/gesAGwKCI+meOnAF/Jz/Y/I+J7Of4RStm4zfJe/j4iXspa1P+R52nL651NJxwg90ePdb1Lp/ZYGxdhZmbWJ3TWanqIpEMiYmbd+CnAcxGxG0DW5n0jt70M7Cppk4h4Ffg0pXlHlyTtCxwG7BkRKyS9l/ZmIOcB7wd2y0Yhg4Gv57yBwA/zXK2U1tczIuJBYDxwawb94/P9qZJ2ptQx3oXSKOQWSTtlLeSJlPrLd1AC5FGUWsjjgCURMVzSGEpb56MzCD+LEqAGcE+efwlwDfAPeQ8/pdR8npi1nn9E+cXiaUlD8l52pQTHI4HXgRsl/TobhPwY+EZE/EbSl4BvAt/K/YmI3fI4MyV9tNqYpJ5bTfdHj/bwy8zMzAAuon1VtGoolaA3Ih6JiBWV7TOBz+TrY3hru+lGhlJWUlfkcRdFxDO5Yv0V4J8j4rXctiwizs55I4GFEfF4RLwOTAVG57bRlIYi5PfPVcanRsSKiHiC0pRkZLaj3jwi5kRppnFV3Zzasa4FDshmKQcDsyJicQbFsyhBNRFxQyTKCnKtjfUXKE1Dns79ak09PgzcERGvRMRK4DfA4bntg5ROhOQ5aq2mdwZurRznz5RgvSGvIPdDzx7ds/luNW1mZs1C0vG0d6MDmBQRkyrv6zvpfTsipuXrOcDhkvYHllX2mQzcLOlISmA2pdICGUqAeqakX1HSMCYDn+jG5d6c8x4FbgGmRcRvKP80Px0RyxrMGwb8sfK+Fdg7X28dEc8CRMSztZXanHNH3ZxhlJXw1g7GVztPdu9bSumG19H5h1XeI2kD4IuU1XeAnYANMpVlMPD9iLgKmA+cL2krylNVhwJzc858Sovp6ympJtvm+P3AaElTc2yv/H7XWz+qwgFyP+Rn9MzMzIoMhid1sktnKRZQUhvOAE6tHPM+STsCBwEHUlIa9o2Ih3L7A5K2p6we31B/SY0vNZZn/u8ngP2BaZkWcW91R0nHUQLNrYCPQYfdArvqgNtoTmfHejtzan4E3B4Rv833gyiB7AGUXOc5ku6IiIckXUhZIV5OCX5X5pwvAT+QdCYwg5KCAeUXkA9TAumngP+uzOmQA+R+aMff9/AAXkI2MzMDICJuk3QusE/d+HLgOuA6SW2Ulc6HKrvMAC4G9qMEsjUvUlIpqgZT0gLIHODZwGxJ84CxwHRgO0mDM7XiCuAKSfMpD+W10r6aCiWN4Zl8/Zykobl6PBSopTI0mtNKexpE/bFqc1rzocEtgMU5vl/dnNm1N5LOAt7H6mUEWinpJC8DL0u6HfgI8GhEXA5cnnMvyH2JiIcpv5QgaScyjSVTMf61cr7/posnshwg90Nn/Oq8Hs2fetRauhAzM7PmcD6lesLjAJI+DjwYEUuygsLOVALCNBlYGhHzslpDze3ANZImRMQySUcA90fEqnzYr62SrrEH8FREvCLpcuBSSSfkQ3oDaX+A725ghKQdKLnRYyg5vlAC9bHAhPx+fWX8p5K+Q3lIbwRwV17HMkn7AHcCxwKX1B1rDnAkcFtWt7gJuEDSe3K/g4DT8rP6MiVH+YC6h+auz/sZlPexN/DdnDMkIp6XtB1wBNmhoTI+gLKqf1mObwooIl6W9GlgZT6g2JAD5H7ovK06ep5gTUxdK9dhZmbWB9TnIN8YEeOrO0TEDZJeqAx9gFKJQZSCCL8GflE3p5VSdo268QckXQr8LkvCPU+p7AClfNklWeFhJeXBuVr+9OnAucB8ScsoGZVTgGcyH/hk4CbKivLkiFiQ8yYA0yWNA56m5O4SEQskTQcezHOdlKvXACfSXuZtZn5BWdW9WtJCysrxmDzW4lxlvzv3OyciFufryyhpD3PKx8V1EXFOplLcCDxAKc3244iYn3N+kTnIb+R1LcnxYySdlK+vA67I10OAm3Il/0+UXOdOqTw0aP3Jwv8xpkc/9OHfmfqWXKLXVnaZy2RmZtbrbDyow/xY6+e8gtwPvf5P6/sKeocIkP+3aGZmZnUcIPdDrV3v0qmd18pVmJmZmfVODpD7oR2Xr+8r6B28emxmZmYdcYDcH/26h/P3WBsXYWZmZtY7OUDuh844+pgezXcNC7N1qy2CAf4Th1mvIGkVMK8yNDUiJmSHt80ioiX3awEujoj9sqzYf1K65IlSw3hUNvoI4CcR8cWcNwh4FrgzIg6TdDawPCIurlzDk0BLRCySdDqlRNsqSnWHEyLizjzOOZQqFC/n1J9HxPl5jFGUqhkDKRUhJuT4lsA0YHvgSeDztaoQkk4DxuW5vhYRN+X4XrRXsbgBOCXLuW1EaT29F6We89ER8WTOGUt7W+7zImJKjl9Dafv8BqWz3QkR8UZu2w/4HrABpSbyJ7PUXa2TIcCOwJkR8T1JFwF/S2kQ8gfguIj4s6S/B75ZmbM7sGdE3EcDDpD7ofNe6G7L9waGj+56H7N+aG09+Ong2KxX6ayT3pD/n733D9uyKvO9P18hFRM1f8agjhpoqTSOEmK97jR/oVmk2wLbJRmmNVrutjXg4KijWDhaTWnSZhJEIx+IKHlLFNRN1gwoWsgvUxlFfdIxEcIHBRT4vn+s8/a5uH1+MD68W+Q+P8dxH891rbXOtdZ13YeH531yrvMr6VTbM+raLwZesN0PIJy616PvFeBwST1srwFOopQe6xRJxwCnU5y7dZL2pLXW8WjgvUC/qIPcE7gk7LoBP4y1minKftOjFvBI4N5w+kfG/QhJh1LKtB1GqYN8j6SDo9TbWEp5ubkUB3kQpdTbcGCl7T6ShgLXAkPCCb+C4ggbeDjWXwlMAj4Xz/BTSkm7sVHK7ibKD4tnahLYth8j/i07nutPwC/CfhZwaZS1u5ZSa3mE7UmxDpL6AXd05BxDOsgNyT/v1TX7jvQ4k6SRSb82SRqO6yhR0XoHuRelti/whlNXZQZF5W0qRW76dop8dGf0okRS18W8y+ENIYwvAQfYXht9LcCVYTcAWGq7JmTSBAym1DgeTKvK3USKoMmIaG+KtZ6K2sYDIpq9i+05MdetwKfimQZX1pxKEfoQRQhkVq32saRZFKf6dttvSG1LepBWlb7PUmoiPxPPU1P4q3IC8B+2n44xMyt9cyliJfXU3neHpIPcgPz9TV2c4LtbZBtJkiRJ8rYj6XxaxTYAxtmuxoLqhUK+bbv2T/xzgDMkHQ+0VMaMB2ZKOgu4F5hYUb+Dkq14uaRfUf65fzyb5yDPDLvHgXuAybZ/A/QBngmnuC16A89W7pspynQA+9h+HiDkpveu2Myts+lNiYQ3t9G+yToRxV1FkdFua/3elXskvYsi4HFxNB0MvCtSWXoC37d9a91zDaV9Z/eLbJqKUWMIxZHvkHSQG5DL/m6XLtlnDnKSJEmyrRDOcEf/ONpRigWU1IbLKFHX2pzzJR1EkVQ+kZLScIztR6N/gaQDKNHMO+vma094y5HDfBTFmT4emBxpEb+vDpR0LsXR3AP4MLQphtKZwFd7Nh3N9VZsatwE3G/7t3HfnZLLfAIl13mOpLm2HwcICe9PEpLVm2y85GmvJ9IqKu1HA69WFPnaJR3kBuSLvPx2byFJki6QIjdJsvVg+76QUR5Y176aInc8LSSOTwMerQyZDlxPSW/Yo9L+EiWVokpPykE/Igd4NjBb0kJgGDAF2F9ST9stticAEyQtohzKawb2q8y3L/BcXL8gqVdEj3tRpK3pwKaZ1jSI+rlqNs1xaHBXiuR0M61pHDWb2bUbSVcAewEXVMY0U9JJXgFekXQ/8DfA49F/KvB72y9UX1QcBjwdOMFvlovuKOK8CdttzqAkSZJk6yGd4yTZ6rgG+PvajaSPSHpPXG9P0dh6us5mPHCV7YV17fcDn4xDdkg6E3jE9gZJh0jqWxl7BPC07VeBmyk5vzuGXTdaD/DNA/pKOjD2M5TioBN/h8X1MOCOSvtQSTtIOhDoCzwY6RgtkgZGfvE5dTa1uc4C7gsn9W7gZEnvifdycrQh6TxKjvLZtjdWnu0O4FhJ3SPH+mg2/YHxplziqNQxAvhkvJNq33aUCh+b9Q/hGUFuQMZPHt0l+5NHbaGNJEmSJMnWT30O8l22R1YH2L5T0ouVpvdRKjGIEoz8NfDzOptmStk16toXSLoR+F2UhPszpbIDwM7ADVHhYT2wlNb86VHA1cAiSS3AGsqhu+ciH/giilPaDRhve3HYjQGmSBoOPENxIrG9WNIUykG+9cCFEb0G+AqtZd5m0HpI8WbgtjjQt4LiiGN7RUTZ58W4q2oH9oAfUX48zCmvi2m2r7L9qKS7gAWUcnY/rqVGhMN8EptGnAFuBHYAZsVcc21/Ofr+G9BcO6jYGXpz9DnZ1nl46dAufelH9Wl6U/xq7fpOc5mSJEmSZKtjx+5t5scmDU5GkBuQXe/p4gR9tsg2kiRJkiRJtkoyBzlJkiRJkiRJKmQEuQGZd2LX7DOAnCRJkiTJtkw6yA1Ij7d7A0mSJEmSJFsx6SA3IIdnDnKS/JfIusNJ0rhI2gBUS7E12R4TCm872+4f4/oD19s+Lqos/CtFJU+UGsaDQujDwE9sfz7sugPPAw/YPl3SlcBq29dX9rAM6G97eYhgfBbYQKnucIHtB2KeqyhVKF4J05/ZvibmGESpmtGNUhFiTLTvTlGcOwBYBnzG9srouxQYHmt9zXatNNtRtFaxuBO42LYl7QDcShH4eAkYYntZ2AyjCKoAjLY9MdonAf0pCn0PxvO8Xnn2D1EU/YbYnlpp7wY8BPzJ9unRdjVFJW8jpfrHF2w/J2kPivT1h4BbbF9EJ6SD3IBcduKBXbJPJb13JunkvXXyvSVJQ9ORkt7ekk61PaOu/WLgBdv9ACQdQnEAoTivh0vqYXsNpVzZnzZnI5KOoYhgHGl7naQ9aa11PBp4L9DP9tqoo3xJ2HUDfhhrNVOU/abbXgKMBO4Np39k3I+QdCilTNthwF8B90g6OEq9jaWUl5tLcZAHUUq9DQdW2u4jaShwLTAknPArKI6wgYdj/ZUUtbvPxTP8lFLSbmxl39cSNZPbeMePAlV54Ots/2PYfg24HPgysBb4R+Dw+HRKOsgNyOjvPNW1CcZumX0k/3dJJy9JkmSLcx0lKlrvIPeiIgxi+7G6/hnAxylRzZrgxbGbsV4virrcuph3ObxRF/hLwAG210ZfC3Bl2A0AltZqAEtqokRal8Tf42LcRIrC3Yhob4q1noraxgMimr2L7Tkx163Ap+KZBlfWnEoRLhFFCGRWrfaxpFkUp/p2229IbUt6kE1V+r5KqR/9oepLkLRvvL9rgP9Va7ddlQp+NyFnHWp8v5O02f8GnlUsGpE1XfwkSZIkyTaCpPMlPVT5nF83pIek+ZXPkErfHGCdpOPrbMZTorBzJI2uU7+D8o+xQ0P17oPAA5u53ZnAfpIel3STpI9Gex/gmXCK26I38GzlvjnaAPYJdTzi796d2PSO67bmesPG9npgFUVGu6P1AZD0LuDzwF1x3xs4gyIkUs+/UJQLN9Z3SLpG0rPA/6BEkN8SGUFuRD7+dm8gSZIkSbYObI8DxnUwpKMUCyipDZdRoq61OedLOogiqXwiJaXhGNuPRv8CSQdQosd31s3XnvCWI4f5KEq0+XhgcqRF/L46UNK5lBSEPYAPQ5tiKJ0JfLVn09Fcb8Wmxk3A/bZ/G/f/AowIie3WTUmnA3+2/bCk4940qT0KGBX50xdRUjv+y6SD3IhkGYskSZIk2SLYvi8Ohw2sa18NTAOmSdoInEbJma0xHbiekt6wR6X9JUoqRZWelIN+RA7wbGC2pIXAMGAKsL+knrZbbE8AJkhaRDmU1wzsV5lvX+C5uH5BUi/bz0vqRTncRgc2zWyaBlGdq2bTHIcGd6VITjfTmsZRs5ldu5F0BbAXm0pH9weawjneEzhN0nrgaOCTkk4DdgR2kfQT259jU35KkfhOBznZPO56f9fsOz36mSRJkiSNxTWUVIBaju9HgCW2V0raHjiUikMYjAdW2V5YFwm9H5gkaYztFklnAo9EJPUQYKPtJ2LsEcDTtl+VdDMl5/eCOKTXjdYDfPOAvpIOpBwIHEqphAHFUR8GjIm/d1Tafyrpu5RDen2BB2MfLZIGUlJDzgFuqJtrDnAWcF9Ut7gb+Jak98S4k4FL412dR8lRPsH2GykTtt+oKCDpFuBXtn8J/LJiexzwjZpzLKlv5d18Evgjb5F0kBuQPTofkiRJkiRJoYek+ZX7u2yPrA6wfaekFytN7wPGxgG17SiRzJ/X2TRTyq5R175A0o2UQ2WmRHTPi+6dgRsk7QasB5ZSqkkAjAKuBhZJaqGcGpoIPGd7vaSLKNUgugHjbS8OuzHAFEnDgWcoZeKwvVjSFMpBvvXAhRG9BvgKrWXeZtB6SPFm4LY40LeC4ohje0VE2efFuKtqB/YoPyyeBuZEtHia7avq38tmMqb2IyLm/HKto3a4ENhe0qeAk6OKR5vI7iwFJdnWWHrN0C596X1GNb0pl2jt+k5zmZIkSZJkq2PH7m3mxyYNTjrIDcjQpb/t0pfe1OfYdJCTJEmSbYJ0kJO2yBSLBmT0PT/s2gR9NqdUY5IkSZIkyTuTdJAbkaxikSRJkiRJ0i7pIDciKfaRJEmSJEnSLukgNyBPntg1+83WaUy2SuyUnU6SJNlcJG0AFlaammyPkTQb2Nl2/xjXH7je9nEh/fyvFJU8UWoYDwqhDwM/sf35sOsOPA88YPt0SVcCq21fX9nDMqC/7eWSRlFKtG2gVGu4wPYDMc9VlCoUr4Tpz2xfE3MMolTN6Ab82PaYaN8dmAwcACwDPmN7ZfRdCgyPtb5m++5oP4rWKhZ3AhdHObcdgFuBoyj1nIfYXhY2wyiCKgCjbU+M9pspNY8FPA58Id7T/6BVfGU18BXbj0SVismV7+Mg4HLb/yLpOuATwGvAfwDn2v5L5T3uT6nKcWX1/bZFOsgNyEHf6eIEY7fINpK3iXSOkyRJ/kt0pKS3t6RTbc+oa78YeMF2P4Bw6l6PvleAwyX1sL0GOIlSm7hTJB0DnA4caXudpD1prXU8Gngv0C/qIPcELgm7bsAPY61mirLf9ChzNhK4N5z+kXE/QtKhlDJth1HqIN8j6eAo9TaWUl5uLsVBHkQp9TYcWGm7j6ShwLXAkHDCr6A4wgYejvVXAl+3/XLs87sUuYUxwFPAR6OW9KkUtcOjbT9Gqf9ce64/Ab+IdzALuDTK2l1LqZf8hsIh8D1aS9J1SDrIDciqS97uHSRJkiSdkf/a847gOkpUtN7p6kWpwwtAOHVVZgAfB6ZS5KZvp8hHd0YvYLntdTHvcoCIWH8JOMD22uhrAa4MuwHAUts1IZMmYDAlmjqYVpW7iRRBkxHR3hRrPRW1jQfU6gnbnhNz3Qp8Kp5pcGXNqRThElGEQGbVah9LmkVxqm+vOMeiRKQd+//3ynPPZVP1vhonAP9h++mwmVlnc1btJmofP0lrdL1DttucQcm2xUtd/CRJkiT//5PO8f8dJJ0v6aHK5/y6IT0kza98hlT65gDrJB1fZzOeEoWdI2m0pL51/U3AUEk7UtIwHtjM7c4E9pP0uKSbJH002vsAz4RT3Ba9gWcr983RBrCP7ecB4u/endj0juu25nrDxvZ6YBVFn6yj9ZE0AfhP4P20qvJVGU7bkd+hlB8XbfHFmo2kd1Oc/n9qZ+ybSAc5SZIkSZKGxfY42/0rn3F1Q9bYPqLymVzXP5rW3NranPMpubHXAbtTUho+UOlfQMn5PZuSorCJeftb9WpKfu/5wIvAZElfqB8o6dxw5p+VtB+0Weu5M/2C9mw6muut2GD7XEoax6NA9QcI8eNjOJumShAS3p8EfvamjZc87fXApGj6J+B78f42i0yxaEAyBzlJkiRJtgy27wsZ5YF17auBacA0SRuB0ygOYI3pwPWU9IY9Ku0vUVIpqvSkHPQjcoBnA7MlLQSGAVOA/SX1tN1iewIwQdIiyqG8ZmC/ynz7As/F9QuSetl+XlIvirQ1Hdg0s2m6Q3Wumk1zHBrclSI53UxrGkfNZnb1AW1vkDQZ+CYwAUDSB4EfA6farv9H7FOB39t+odoYhwFPB05wqxre0cBZkv4Z2A3YKGmt7Rtph4wgNyI9uvhJkiRJkqTKNcDf124kfUTSe+J6e+BQKjnJwXjgKtsL69rvBz4Zh+yQdCbwSDiQh9SlaxwBPG37VeBmSs7vjmHXjdYDfPOAvpIOjP0MpTjoxN9hcT0MuKPSPlTSDpIOBPoCD0YaRoukgZE3fE6dTW2us4D7wkm9GzhZ0nvivZwM3K1Cn9ivKBUo/hj3+1N+YHze9uNveuOtudtvEJU6RgCfjHcCgO1jbR9g+wDgX4BvdeQcQ0aQG5Osg5wkSZIkm0sPSfMr93fZHlkdYPtOSS9Wmt4HjA2nbzvg18DP62yaKWXXqGtfIOlG4HdREu7PwHnRvTNwg6TdKCkESynpFgCjgKuBRZJaKP+3nwg8F1UdLqI4qt2A8bYXh90YYIqk4cAzlDJx2F4saQrlIN964MKIXgN8hdYybzNozQ++GbgtDvStoDji2F4RUfZ5Me6qaNsOmChpF0oaxiMxN8DllMj6TeU1sr5SUm8nSkWOC+pe343ADsCssJlr+8v173hzUGv0OWkUls4f2qUvvc8RTW/KJVq7vtNcpiRJkiTZ6tixe5v5sUmDkxHkBuSyl0Z3yb5pC+0jSZIkSZJkayQd5AZk8F9f1vmgDkkXOUmSJEmSbZd0kBuQD/2hixOk1nSSJEmSJNsw6SA3IlmJIkmSJEmSpF2yzFsDMu/9XfskSZIkSaMgaUOdkt7IaJ8t6aHKuP6SZsf1TpImSVooaZGk30naOfos6baKXXdJL0r6VdxfKekbdXtYJmnPuB4labGkBbGfoyvzfEvSE5W9jqrMMUjSY5KW1p4h2neXNCvsZtXK00XfpTH+MUmnVNqPimdbKukHUa2DKAk3OdofkHRAxWZYrPFE1Cqutd8i6anKno+oe/YPxXdQlY3+eryDRZJur5S2+3S0b5TUv43vcn9Jq+vfb1tkBLkBuYNvdsn+7C20jyRJkiR5B7DG9hHt9O0t6VTb9TLIFwMv2O4HIOkQ4PXoewU4XFIP22so5cr+tDkbkXQMRQTjSNvrwmmu1ToeDbwX6Gd7rUod5UvCrhvww1irmaLsN932EmAkcK/tMeE4j6TIZB9KKdN2GEXl7h5JB0ept7GU8nJzKUqAgyil3oYDK233kTQUuBYYIml34AqgP0VB7+FYf2Xs/Zu2p7bxvN1ijrsrbb2BrwGH2l4TpeiGUsrOLQLOBP53O6/we7QtWf0m0kFuQEZ/57quTTA2D+klSZIkCUVK+jLe7HT1oiIMYvuxuv4ZwMeBqbQKXhy7Gev1ApbbXhfzLoc36gJ/CTjA9troawGuDLsBwFLbT8b4JmAwpcbxYFpV7iZSFO5GRHtTrPVU1DYeIGkZsIvtOTHXrcCn4pkGV9acShEuEXAKMMv2irCZRXGqNxH6aIOvUupHf6iuvTulPvXrwE6Ekp/tR2P+N00k6VPAk5QfKJ2SDnIDsuqSt3sHSZIkSbJ1IOl8WsU2AMbZHle5rxcK+bbtyXE9BzhD0vFAS2XMeGBmpAXcC0y0/USlvwm4PNIqPhjjN8dBnhl2jwP3AJNt/4ZyfP6ZcIrbojfwbOW+mSK/DLBPqOMRctN7V2zm1tn0pkTCm9to32SdECdZRRH7aGv93pX7ayRdTnlXIyM63hs4A/gYFQfZ9p8kXU8RNVkDzLQ9s53nBkDSuylO/0lAp+kVkDnIDclLXfwkSZIkybaC7XG2+1c+4+qGrLF9ROUzua5/NCWKXJ1zPnAQJcK8OyWl4QOV/gXAAZTo8Z31W2p/q14NHEVx6F8EJkv6Qv1ASedGPu+zkvaDNsVQOhP4as+mo7neis2lwPspTvDuFEcWiiT0iIp6X1mg5EgPBg6kpH68W9Ln2n2Kwj8B34v3t1lkBLkBOSjLvCVJkiTJFsH2fSoyygPr2lcD04BpkjYCpwGPVoZMB66npDfsUWl/iZJKUaUn8JeYdwMlDWK2pIXAMGAKsL+knrZbbE8AJkhaRJGWbgb2q8y3L5GWALwgqVdEj3tRpK3pwKY5rtuaq2bTLKk7sCtFcrqZ1jSOms3seJ7no22dpAm0Rnj7A02RLrEncJqk9cC7gKdsvwggaRrwYeAntM/RwFmS/hnYDdgoaa3tG9szSAe5AVn1t2/3DpIkSZJkm+Ia4EeUHFckfQRYYnulpO2BQwmHsMJ4YJXthZKOq7TfD0ySNMZ2i6QzgUdsb4jDfhsr6RpHAE/bflXSzZSc3wvikF43Wg/wzQP6SjqQciBwKPDZ6JtOcbLHxN87Ku0/lfRdSqS2L/Bg7KNF0kDgAeAc4Ia6ueYAZwH32baku4FvVSpknEyJHFNxzkXJZV4EYPvA2guRdAvwK9u/VKnaMTDyrtcAJwBvVBNpC9tvpK9IuhJY3ZFzDOkgNySPd9H+qC2yiyRJkiR5R1Cfg3yX7ZHVAbbvlPRipel9wNhw+rYDfk05bFa1aQa+X7+Y7QWSbgR+J8mUiO550b0zcIOk3YD1wFJa86dHAVcDiyS1UJzHicBzkQ98EaUaRDdgvO3FYTcGmCJpOCWv99Oxj8VRIWJJrHVhJd3hK5SqET0oh/NqhxRvBm6LA30rKI44tldElH1ejLuqdmCP8mNgL0oaxnzgy/XvpO79PCBpKvD72NcfgHEAks6gOOt7Ab+WNN/2Ke1O1gGyO0tBSbY1ls4f2qUvvc8RTW/KJVq7vtNcpiRJkiTZ6tixe5v5sUmDk4f0kiRJkiRJkqRCplg0IE/u3DX7PKOXJEmSJMm2TDrIDchB3+niBGO3yDaSJEmSJEm2StJBbkQ+9nZvIEneWdjQhjBTkiRJso2SOchJkiSdkM5xkjQukjaE6EbtMzLaZ0t6qDKuv6TZcb2TpEmSFkpaJOl3knaOPku6rWLXXdKLoaqHpCslfaNuD8sk7RnXoyQtlrQg9nN0ZZ5vSXqistdRlTkGSXpM0tLaM0T77pJmhd2sSik2JF0a4x+TdEql/ah4tqWSfhDVOpC0g6TJ0f6ApAMqNsNijSckDau03yzpkXieqbX3VOn/UHwHZ1Xaxkv6c9R5ro69uvJeZkr6q2g/QNKaynv5UUffOWQEuTH5dRftP71FdpEkSZIk7wTW2D6inb69JZ1qe0Zd+8XAC7b7AUT94tej7xXgcEk9bK+hyB//aXM2IukY4HTgyJBj3pPWWsejgfcC/aIOck/gkrDrBvww1mqmKPtNt70EGAnca3tMOM4jgRGSDqWUaTuMUgf5HkkHR6m3sZTycnMpSoCDKKXehgMrbfeRNBS4FhgiaXfgCor4h4GHY/2VwNdtvxz7/C5wEaX0XG3f11LK01W5BbgRuLWu/Trb/xi2XwMup7Vs3H908D2+iYwgNyI9uvhJkiRJkgSKlPRlbbT3ouL02n7M9rpK/wzg43F9NnD7Zq7XC1hem8v2ctvPhWjGl4Cv2l4bfS22rwy7AcBS20/afg1oosg1E38nxvVEilhHrb3J9jrbT1FqLg9QUdvbxfYcl1rBt9bZ1OaaCpwQ0eVTgFm2V4RTPIviVFNxjkXxMqplY79KqR/950obtu+n1Fmmrv3lyu276VxOu10ygtyI/M3bvYEkSZIk2TqQdD6tYhsA42yPq9zXC4V82/bkuJ4DnCHpeKClMmY8MDPSAu4FJlbU76A4qJdHWsUHY/yxdM7MsHscuAeYbPs3lAJTz9huaceuN/Bs5b6ZIr8MsE9N7jkU7fau2Myts+lNiYQ3t9G+yTohTrKKIqPd1vo1G1Qkpk+jiJLUot69gTMoJ6c+1M5zvQlJ11DU/VYBx1e6DpT0B+Bl4DLbv+1onnSQG5B5J3bNPsu8JUmSJNsK4QyP62BIRykWUFIbLgNGVOacL+kgiqTyiZSUhmNsPxr9CyI/92xKisImW2p/q14t6SiKM308MDnSIn5fHSjpXEqaxx7Ah6FNMZTOoqvt2XQ011uxwfa5kU5xAzAEmAD8CzAipK072WplUnsUMErSpZR0jSuA54H9bb8U7++Xkg6rizhvQjrIDcgjXbQ/e4vsIkmSJEne+di+T0VGeWBd+2pgGjBN0kZKhPTRypDpwPXAcRRHtsZLlFSKKj2Bv8S8G4DZwGxJC4FhwBRgf0k9I7ViAjAhDrF1o0Rs96vMty/wXFy/IKlXRI970ZrO0J5Nc1y3NVfNpllSd2BXSipEczxn1WZ29QHDEZ4MfJPiIPcHmsI53hM4TdJ6279k8/gp5dTVFZGSUktLeVjSfwAHAw+1Z5wOcgMysPMhSZIkSZJsPtcAPwKeBJD0EWCJ7ZWStgcOpc4hpKRVrLK9UNJxlfb7gUmSxthukXQm8Eg4kIcAGyvpGkcAT9t+VdLNwI2SLohDet1oPcA3D+gr6UBKbvRQ4LPRN53iZI+Jv3dU2n8aB+f+CugLPBj7aJE0EHiAks5wQ91cc4CzgPtsW9LdwLcqFTJOBi6NvOP32V4a158A/ghg+8DaC5F0C/CrzpxjSX0r7+aTtbkk7QWsiL0fFM/yZEdzpYPcgBx+TxcnyByLJEmSpHGoz0G+y/bI6gDbd0p6sdL0PmBsOH3bUSKZP6+zaQa+X79YpF/cCPxOkikR3fOie2fgBkm7AespB+dq+dOjgKuBRZJagDWUA3PPRT7wRZRqEN2A8bYXh90YYIqk4cAzRK0q24slTaHkBa8HLozoNcBXKJUkelAOHNaqeNwM3CZpKSVyPDTmWhFR9nkx7qpo2w6YKGkXShrGIzF3h0i6nRKR3lNSMyVKfDMwpvYjAnia1goW/w24StJ6YAPwZdtvOuS3yRrlAGLSSNy+dGiXvvSz+zS9KRlo7fq3flI0SZIk+a+TAjZbhh27t5kfmzQ4GUFuQDIHOUmS5J1POsdJ8v8f6SA3IOeN7uIEt2yJXSRJkiRJkmydpIPciOz+dm8gSZIkSZJk6yUd5EYkhUKSJEmSJEnaJaWmG5DXPtK1T5IkSZI0CpI2SJpf+YyM9tmSHqqM6y9pdlzvJGmSpIWSFkn6naSdo8+SbqvYdZf0YqjqIelKSd+o28MySXvG9ShJiyUtiP0cXZnnW5KeqOx1VGWOQZIek7S09gzRvrukWWE3q1KKDUmXxvjHJJ1SaT8qnm2ppB9EtQ4k7SBpcrQ/EGIoNZthscYTkoZV2g+MsU+E7fbRfpykVZVnuTzad5T0oKRH4j38U927+mrsd7Gkf+7sWdojI8gNyPaTOx/TIaM6H5IkSZIk2wgdKentLelU2zPq2i8GXrDdDyBKj70efa8Ah0vqYXsNcBKlNnGnSDoGOB040va6cJprtY5HA+8F+kUd5J60yjZ3A34YazVTlP2m214CjATutT0mHOeRwAhJh1LKtB1GqYN8j6SDo9TbWEp5ubkUJcBBlFJvw4GVtvtIGgpcCwyRtDtF0a4/RUHv4Vh/ZYz5nu0mST+KOcbGM/3W9ul1r2Ed8LFQFXwXpRzeDNtzVSS/BwMfjPezdzx/R8/SJhlBbkQO7uInSZIkSRKA6ygy0/X0ouL02n4s1NxqzAA+HtdnA7dv5nq9gOW1uWwvt/2cpJ2ALwFftb02+lpsXxl2A4Cltp+0/RrQRHEkib8T43oi8KlKe5PtdbafotRcHqCitreL7TkutYJvrbOpzTUVOCGiy6cAs2yvCKd4FjAo+j4WY+vXbxMXVsftu+JTKzX7FWBM5f3UVAHbfJaO1skIciPy8y93PqYjPr1ltpEkSZIk7wDqhUK+bbv2b7FzgDMictlSGTMemCnpLOBeYGJF4Q2Kg3p5pFV8MMYfuxl7mRl2jwP3AJNt/4Yi4fWM7ZZ27HoDz1bum4Gj43of288DhNz03hWbuXU2vSmR8OY22jdZJ8RJVlFktNtav3f0/cX2+jbmAjhG0iMUKetv1MRNIiL+cDz3D20/EOMPBo6VdA2wNmzmdfAs7ZIOciMy8kddnOC4LbGLJEmSJHnbkXQ+rWp0AONsj6vcd5RiASW14TJgRK3B9nwVSeOTgRMpKQ3H2H40+hdEfu7ZlBSFKu0JbznSCo6iONPHA5MjLeL3dc90LiXNYw/gw9CmGEpnAl/t2XQ013/VpqO5fg/8dTzzacAvKRLRRGrEESqKgr+QdLjtRRS/9j3AQOBDFIXAgzpZp03SQW5E5nY+pEOO2BKbSJIkSZK3n3CGx3U6sH37+1RklAfWta8GpgHTJG0ETgMerQyZDlxPiTrtUWl/iZJKUaUn8JeYdwMwG5gtaSEwDJgC7C+pZ6RWTAAmSFpEkZZuBvarzLcvJSoL8IKkXhE97kWRtqYDm+a4bmuumk2zpO7ArhTJ6WY2ja7tG8+wHNhNUveIIr8xl+2Xa4NDyvsmSXvaXl5p/4vKwchBwKJYZ1qkfjwY733PTp6/TdJBbkCePLFr9n22zDaSJEmSZFvhGuBHwJMAkj4CLLG9MqoyHEpxCKuMB1bZXijpuEr7/cAkSWNst0g6E3jE9oY47Lexkq5xBPC07Vcl3QzcKOmCOKTXjdYDfPOAvpIOpORGDwU+G33TKU72mPh7R6X9p5K+SznY1hd4MPbRImkg8ABwDnBD3VxzgLOA+2xb0t3AtyoVMk4GLo2+/xNjm6rrS3ov5aCjJQ2gnJt7SdJewOvhHPegROivjXl/Sclpni3p4Hj+5e09Cx2QDnIDctDqzsckSZIkSQK8OQf5LtsjqwMiwvlipel9wNg4hLYd8Gvg53U2zcD36xeL9IsbKdUZTInonhfdOwM3RGrBesphs1p6yCjgamCRpBZgDeXQ23ORD3wRcDclojy+ls9LcYynSBoOPEOcNLK9WNIUYEmsdWGl6sNXKLq6PSgHDmtVPG4GbpO0lBI5HhpzrYgo+7wYd5XtFXE9AmiSNBr4Q8wBxWn+iqT18SxDw1nuBUyMHwDbAVNs/ypsxgPjI3L+GjAsoskdPUubqNgljcTSOUO79KX3OabpTbk8a9d3msuUJEmSJFsdO3ZvMz81aXAygtyINHc+JEmSJEmSpFFJB7kRefzt3kCSJEmSJMnWSzrIjUiKfSRJkiRJkrRLOsgNyLy/7Zp9VrFIkiRJkmRbJqWmkyRJkiRJkqRCRpAbkA/d08UJMoScJEmSNAiSNgALK01NtseEQMXOtvvHuP7A9baPk7QT8K8UGWlRRD4GhSqcgZ/Y/nzYdQeeBx6wfbqkK4HVtq+v7GEZ0N/2ckmjKDWMNwAbgQtsPxDzXEUp0/ZKmP7M9jUxxyBKWbluwI9tj4n23YHJwAHAMuAztldG36XA8Fjra7bvjvajaC3zdidwcZRg2wG4FTiKIngyxPaysBlGURwEGG17YrRfBPxPSmm8vWpCIFEzeXy0rwW+GGp5SPo6pfSd47s51/ba6PsqcBGlnNuvbf+9pHcBPwaOpPi+t9r+9pu+7AoZQU6SJEmSJGmfNbaPqHzGVPr2lnRqGzYXU0Qu+tk+nOJkvh59rwCHh8gFwEkU8Y5OkXQMcDpwpO0PUkQyno3u0RQRjH4hjX0s8K6w6wb8EDiVIlpytqRDw24kcK/tvsC9cU/0DwUOoyjV3RTzAIyl1F/uG59B0T4cWGm7D/A9QsAjnPArgKOBAcAVFdGQf4vneLrucf8BmB/PeQ5RM1pSb+BrlB8Mh1Mc/qHRdzwwGPig7cMoSoVQfjTsYLsfxXm/IKS+2yUjyI3II2/3BpIkSZJkm+A6SlR0Rl17LyoOn+3H6vpnAB8HpgJnA7dTHNrO6AUst70u5q1FW3cCvgQcUIuk2m4Brgy7AcBS2zWlvyaKI7kk/h4X4yZSFP9GRHtTrPVUiH8MiGj2LrbnxFy3Ap+KZxpcWXMqRdlPwCnArJo4iKRZFKf6dtt/iLb6Zz0U+HY8yx8lHSBpn+jrThFweR3YiVbZ6K8AYyrvpyabbeDdEWXvQREReUPKui3SQW5EPvZ2byBJkiRJtg4knU+rGh3AONvjKvf1Snrftj05rucAZ0TksqUyZjwwU9JZlKjsxIo8NBRZ5csl/YqShjGezXOQZ4bd48A9wGTbv6EkPz4TTnFb9KY10gxFEeHouN7H9vMAtp+XtHfFZm6dTW9KJLy5jfZN1gn1vlXAHu2s35uOeQQ4k6IoOAD4a2Bf2w9Lup6i+rcGmGl7ZtgcDBwr6RpKWsY3bM+jOOuDKaksOwFfryj5tUk6yA3IL7pYxeKbW2YbSZIkSfK2E87wuA6GrImUhfYYTYkij6jMOV/SQcDJlPSBeZKOsf1o9C+If+I/m5LDu8mW2t+qV0f+77HA8cBkSSOB31cHSjqXkuaxB/BhaFMtsDMF3PZsOprrrdi0xxjg+/HjZCFFhnp9pGYMBg6k5Hb/TNLnbP+E4te+BxgIfIgioX0QJYK+gZKC8h7gt5LuqUXU2yId5AbkjD90cYI8pJckSZIkANi+T9LVFKes2r4amAZMk7QROA14tDJkOiVH9jiKI1vjJUoqRZWeFGcQ2xsoaRCzJS0EhgFTgP0l9bTdYnsCMEHSIkqObjOwX2W+fWlNS3hBUq+IHvcCamkJ7dk0x3Vbc9VsmiOdYVdgRbQfV2czmw6w/TJwLkCkaTwVn1OAp2y/GH3TKD8CfhLrTLNt4MF473tSDjXeZft14M+S/g3oD6SDnLRy2d+e3CX7pi20jyRJkiTZRrgG+BHhcEn6CLDE9kpJ21PyaWfX2YwHVtleKOm4Svv9wCRJY2y3SDoTeMT2BkmHABsr6RpHAE/bflXSzZSc3wtsr40DddvHuHlAX0kHUg4EDqU4jVAc9WGUiO0w4I5K+08lfZcSee0LPBj7aJE0EHiAcoDuhrq55gBnAfdFdYu7gW9VDuadDFza0QuVtBvwqu3XKBUr7rf9sqRngIGRd70GOAF4KMx+SUkknS3p4Hj+5ZR0jI9J+gklxWIg8C8drZ8OcgMy+jszOx/UEWO/uGU2kiRJkiRbP/U5yHfZHlkdYPtOSS9Wmt4HjI3I53bAr4Gf19k0E5UZ6toXSLqRkntrSkT3vOjeGbghnMf1wFJa86dHAVcDiyS1UJzHicBzkQ98EXA3JaI83vbisBtDSUUYTnEkPx37WCxpCuUg33rgwoheQzkMdwvlwNsMWg8p3gzcFgf6VhDVJWyviCj7vBh3VeXA3teAvwfeCyyQdKft84APALdGmb0llAoZREm7qZS0kvWU1Itaisx4YHxEzl8DhoWD/kNgArCIku4xwfaC+ndfRSUKnTQSS380tEtfep8vN70pl2jt+k5ziZIkSZJkq2PH7m3mxyYNTkaQG5HH3+4NJEmSJEmSbL2kg9yA3PV3XbO/aMtsI0mSJEmSZKskHeQGZFBWsUiSJEmSJGmXdJAbkOe7WAc5/eMkSZIkSbZltnu7N5D836fX6q59kiRJkqRRkLRB0vzKZ2S0z5b0UGVcf0mz43onSZMkLZS0SNLvJO0cfZZ0W8Wuu6QXQ1UPSVdK+kbdHpZJ2jOuR0laLGlB7OfoyjzfkvREZa+jKnMMkvSYpKW1Z4j23SXNCrtZlVJsSLo0xj8m6ZRK+1HxbEsl/SCqdSBpB0mTo/2BEEOp2QyLNZ6QNKzSfrOkR+J5plbe0/+ItgWS/l3S30T7jpIeDJvFkv6pMtd1kv4YNr+Iah9I2l7ShNjzI3Vl9dokI8iNyNzOh3TIEVtiE0mSJEnyjqAjJb29JZ1qe0Zd+8XAC7b7AUT94tej7xXgcEk9bK8BTqLUJu4USccApwNH2l4XTnOt1vFoSqm0flEHuSdwSdh1A34YazVTlP2m214CjATutT0mHOeRwAhJh1LKtB1GqYN8j6SDo9TbWEp5ubkUJcBBlFJvw4GVtvtIGgpcCwyRtDtwBUWcw8DDsf5Kiuzzy7HP71KOOo2hiIJ8NGpJn0op5XY0sA74WKgKvotSDm+G7bnALODSKGt3LaXW8gjgSwC2+6lIac+Q9CHbG9t71+kgNyBPntg1+0yxSJIkSRIArqPITNc7yL2Ap2s3th+r658BfByYSpGbvp0iH90ZvYDlttfFvMuhRKwpTuABttdGXwtwZdgNAJbWpJUlNVHkmpfE3+Ni3ESKoMmIaG+KtZ6K2sYDJC0DdrE9J+a6FfhUPNPgyppTKcIloqjfzarUPp5FcapvrzjHotRVduz/3yvPPZdQ7wuVvNq/Z78rPjWbmXU2Z8X1ocC9MebPkv5CcdYfbOc9p4PciByUh/SSJEmSBABJ59MqtgEwzva4yn29UMi3bU+O6znAGZKOB1oqY8YDMyWdRXHMJlbU76CI0l4eaRUfjPGb4yDPDLvHgXuAybZ/Q/k/8zPhFLdFb+DZyn0zJRoLsI/t5wFCbnrvis3cOpvelEh4cxvtm6wTUdxVFBntttav2SBpAkWKewkR9a5jOJUfIRERfzie+4e2H2jD5otA7Xt6BBgcPwz2A46Kv+kgJ63c1cVDelnmLUmSJNlWCGd4XAdDOkqxgJLacBkl6lqbc76kgyiSyidSUhqOsf1o9C+I/NyzKSkKm2yp/a16taSjKM708cDkSIv4fXWgpHMpaR57AB+GNsVQOhP4as+mo7neig22zw2n9wZgCEX1rkxYfnwMB/6fyvgNwBGRY/wLSYfbXlSxGUVR2ZsUTeMpynwPUSL7/x797ZIOcgPyN2/3BpIkSZJkG8H2fSoyygPr2lcD04BpkjZSIqSPVoZMB66npDfsUWl/iZJKUaUn8JeYdwMlDWK2pIXAMGAKsL+knrZbbE8AJqhILnejRGz3q8y3L/BcXL8gqVdEj3tRpK3pwKY5rtuaq2bTLKk7sCtFcrqZ1jSOms3s6gPa3iBpMvBNwkGW9EHgx8Cptl+qeyfY/ovKwchBFBlp4gDg6cAJkY6B7fXA12t2kv4deKJ+virpIDcgvaZ3cYL/tUW2kSRJkiTbCtcAPwJqOb4fAZbEAbPtKTmws+tsxgOrbC+sq6pwPzBJ0hjbLZLOBB4JB/IQYGMlXeMI4Gnbr0q6mZLze0Ec0utG6wG+eUBfSQdSDgQOBT4bfdMpTvaY+HtHpf2ncXDur4C+wIOxjxZJA4EHgHMokd/qXHMo+b/32baku4FvqbVCxsnApZF3/D7bS+P6E8Af4x3uT/mB8Xnbb2gAS9oLeD2c4x6UCP210TeIEsn/qO1XKzY7AbL9iqSTgPVxQLFd0kFuQJ78ZNfsMwU5SZIkaSDqc5Dvsj2yOsD2nZJerDS9DxgbTt92wK+Bn9fZNAPfr18s0i9upFRnMCWie1507wzcEKkF64GltOZPjwKuBhZJagHWUA7dPRf5wBcBd1MiyuNtLw67McAUScOBZ4BPxz4WS5pCyQteD1wY0WuArwC3UA7VzaA1P/hm4LY40LeC4ohje0VE2efFuKuibTtgoqRdKGkYj8TcAJdTIus3ldfIetv9KdH1ifEDYDtgiu1fhc2NwA7ArLCZa/vLwN7A3RHJ/xPw+fr3Xo8i+pw0EEOXnt+lL72pz7g35RKtXd9pLlOSJEmSbHXs2L3N/NikwckIcgNyIS+/3VtIkiRJkiTZakkHuQHpdU8XJ8gciyRJkiRJtmHSQW5E9uh8SJIkSZIkSaOSDnID8mQX6yBnADlJkiRJkm2ZdJAbkPEc2CX7k7fQPpIkSZJka0fSBmBhpanJ9piov7tzVFZAUn/getvHRVmxf6Wo5IlSw3hQCH0Y+Intz4ddd+B54AHbp0u6Elht+/rKHpYB/W0vDxGMzwIbgI3ABbYfiHmuolSheCVMf2b7mphjEKVqRjfgx7bHRPvuFMW5A4BlwGdsr4y+SykiHRuAr9m+O9qPorWKxZ3AxVHObQfgVopS3UvAENvLwmYYRVAFYLTtidF+EfA/KZU/9qrIZ+8K/ATYn+KvXh/1nZH0dUplD8d3c26UtjuCUm5vR0rljb+z/WCUdhtDKXv3GvBN2/e96cuukA5yA/JNnnq7t5AkSZIk7xQ6UtLbW9KptmfUtV8MvGC7H0DUL349+l4BDpfUw/Ya4CRK6bFOkXQMRQTjSNvrJO1Ja63j0cB7gX7hLPYkZJujJNoPY61mirLf9KgFPBK4N5z+kXE/QtKhlDJth1HqIN8j6eAo9TaWUl5uLsVBHkQp9TYcWGm7j6ShlPrEQ8IJvwLoT3FqH471VwL/BvyKN9eJvpBSS/oTUfv4MUmTgL2ArwGH2l4TpeiGUhz2fwb+yfYMSafF/XHAcuATtp+TdDil3F1vOiAd5AZk19FdnOCWLbGLJEmSJHnHcx0lKlrvIPeiSBoDYPuxuv4ZwMeBqRS56dsp8tGd0QtYbntdzFuLtu4EfAk4wPba6GsBrgy7AcBS2zUhkyZgMKXG8WBaVe4mUhzVEdHeFGs9FbWNB0Q0exfbc2KuW4FPxTMNrqw5lSJcIuAUYJbtFWEzi+JU3277D9FW/6wGeob9zpS6yjV56O6U+tSvAzvRquRnYJe43rXWXlsjWAzsKGmH2ntsi+3a60i2YXp08ZMkSZIk2wiSzpf0UOVzft2QHpLmVz5DKn1zgHWSjq+zGU+Jws6RNFpS37r+JmCopB0paRgPbOZ2ZwL7SXpc0k2SPhrtfYBnwilui97As5X7ZlojqPvYfh4g/u7diU3vuG5rrjdsQt55FaU0QEfrt8eNwAcoTu5CShrHRtt/okh0P0NJTVlle2bY/E/gOknPxphL25j3vwN/6Mg5howgNySrLnm7d5AkSZIkWwe2xwHjOhjSUYoFlNSGyyhR19qc8yUdRDm2cyIlpeEY249G/wJJB1Cix3fWb6n9rXp15P8eCxwPTI60iN9XB0o6l5LmsQfwYWhTDKUzga/2bDqa663YtMcpwHzgY5T85FmSfkvJoR4MHEjJ7f6ZpM/Z/glFhe/rtn8u6TMUZb8T33gg6TBK2kenx6nSQW5A5nTR/qgtsoskSZIkeedj+76QUR5Y174amAZMC4nj04BHK0OmU6Kcx7FpAdaXKKkUVXpSnEEiB3g2MFvSQmAYMAXYX1JP2y1xmG2CpEUUh7IZ2K8y3760piW8IKmX7ecl9aJIW9OBTXNctzVXzaY5Dg3uSkmNaKY1jaNmM5uOORcY4yL5vFTSU8D7gb8GnrL9IoCkaZQfAT+Jd3Fx2P8M+HFtMkn7Ar8AzrH9H52snSkWjcgxXfwkSZIkSbIJ1wB/X7uR9BFJ74nr7YFDqeQkB+OBq2wvrGu/H/hkHLJD0pnAI7Y3SDqkLl3jCOBp269SoqU3RtpG7WBe7QDfPKCvpANjP0MpDjrxd1hcDwPuqLQPlbSDpAOBvsCDkYbRImlg5AefU2dTm+ss4L5wcO8GTpb0nngvJ0dbRzwDnBDPsg9wCPBktA+UtFOsfwKtPzyeA2ppJx8Dngj73YBfA5fa/rdO1gUygtyQ7JpKekmSJEmyufSQNL9yf5ftkdUBtu+U9GKl6X3A2HDgtqM4Zz+vs2mmlF2jrn2BpBuB30VJuD9TSppBOax2Qzh864GllGoSAKOAq4FFklqANZRDd8/ZXh/l1O6mRJTH214cdmOAKZKGU5zPT8c+FkeFiCWx1oURvYaSynAL5WTSDFoPKd4M3BYH+lZQHHFsr4go+7wYd1XlwN7XKD8u3gsskHSn7fPiWW6JKLmAEXEocbmkqZS0kvXAH2hNkfkS8P2IXq+tvJuLKN7LP0r6x2g72XYtWv4mVBz7pJFY+rOhXfrS+3y66U25RGvXd5pLlCRJkiRbHTt2bzM/NmlwMoLcgNzQRSW9N/3cTZIkSZIk2YZIB7kB+epNXZzgu1tkG0mSJEmSJFsl6SA3Ige/3RtIkiRJkiTZeskqFkmSJEmSJElSIR3kJEmSJEmSdpC0oU5Jb2S0z5b0UGVcf0mz43onSZMkLZS0SNLvJO0cfZZ0W8Wuu6QXJf0q7q+U9I26PSyTtGdcj5K0WNKC2M/RlXm+JemJyl5HVeYYJOkxSUtrzxDtu0uaFXazauXpou/SGP+YpFMq7UfFsy2V9IOo1kGUhJsc7Q+EGErNZlis8YSkYZX2myU9Es8ztfKeFHMvjb4j695JN0l/qL23aLtO0h9j/C+i2geS3iVpYuz5UUltKextQqZYNCCrTux8TJIkSZIkQMdKentLOtX2jLr2i4EXbPcDkHQI8Hr0vQIcLqmH7TXAScCfNmcjko4BTgeOtL0unOZarePRlFJp/WyvVamjfEnYdQN+GGs1U5T9ptteAowE7rU9JhznkRSZ7EMpZdoOA/4KuEfSwVHqbSylhNpcihLgIEqpt+HAStt9JA2lqNYNkbQ7cAXQn6Kg93Csv5KifPdy7PO7lJJsY4BTKbWX+wJHx5pH173jR4FdKm2zKLWO10u6liI1PYJSum4H2/0k7QQskXS77WXtvet0kBuQXTerRHYHZB3kJEmSJAG4jiIzXe8g96IiDGL7sbr+GcDHgakUuenbKfLRndELWG57Xcy7HErEmlID+ADba6OvBbgy7AYAS20/GeObKHLNS+LvcTFuIkXhbkS0N8VaT0Vt4wGSlgG72J4Tc90KfCqeaXBlzakU4RJRZKNnVWofz6I41bdXnGNR6irXysYOBm4NoZG5knZTq+LfvvH+rgH+V+3l2J5ZeVdzKWIlxJzvjvrIPYDXgJc7etGZYtGIrOniJ0mSJEm2ESSdL+mhyuf8uiE96lIshlT65gDrJB1fZzOeEoWdI2m0NlW/A2iiqNTtCHwQeGAztzsT2E/S45JuklRTjesDPBNOcVv0Bp6t3DdHG8A+oY5H/N27E5vecd3WXG/Y2F4PrKLIaHe0PpImAP9JkZK+YTP2/C8UcZGN7TwvwBdp/eEylRK5f54ihnJ9zVlvj4wgNyJzu2j/5S2yiyRJkiR527E9jlYltrboKMUCSmrDZZSoa23O+ZIOokgqn0hJaTjG9qPRvyDyc8+mpChssqX2t+rVko6iRJuPByZHWsTvqwMlnUtJQdgD+DC0KYbSmcBXezYdzfVWbLB9bqSB3AAMASa0ZyPpdODPth+WdFybGy+51+uBSdE0ANhASRV5D/BbSffUIuptkQ5yIzLw7d5AkiRJkmwb2L5PRUZ5YF37amAaME3SRuA0Ss5sjenA9ZT0hj0q7S9RUimq9AT+EvNuoKRBzFaRYR4GTAH2l9TTdovtCcAESYso0tLNwH6V+fYFnovrFyqpC70o0tZ0YNMc123NVbNpjnSGXSmS0820pnHUbGZXH9D2BkmTgW9SHOT21j8L+KSk04AdgV0k/cT256AcBqTkaZ/gVrnoz1Ikwl8H/izp3yj50O06yJli0Yg80sVPkiRJkiRVrqH8kz8Akj5SqwYhaXvgUCo5ycF44CrbC+va76c4gD3D/kzgkXAgD6lL1zgCeNr2q8DNlJzfHcOuG60H+OYBfSUdGPsZSnHQib+1qhLDgDsq7UOjMsWBlMNyD0YaRoukgZE3fE6dTW2us4D7wkm9GzhZ0nvivZwM3B2VKvrEfgV8AvhjZa5zYsxAYJXt521fantf2wfEc9xXcY4HUSL5n4x3UuMZ4GMx17spP2b+SAdkBLkBefKSrtnnGb0kSZKkgeghaX7l/i7bI6sDbN8p6cVK0/uAseH0bQf8Gvh5nU0z8P36xSL94kbgd5JMieieF907AzdE+bL1wFJKNQmAUcDVwCJJLZRTQxOB56Kqw0UUR7UbMN724rAbA0yRNJziSH469rFY0hTKQb71wIURvQb4CnAL5cDbDFpzfW8GbosDfSsoDiy2V0SUfV6MuyratgMmStqFklLxSMwNJfXktHjGV4Fz699VG9wI7ADMKq+euba/TKngMQFYFOtMsL2go4nUGn1OGoWlPxrapS+9z5eb3pQXtHZ9p7lMSZIkSbLVsWP3NnNdkwYnI8iNyONv9waSJEmSJEm2XjKC3IAMfWxpl770pkP6ZAQ5SZIk2SbICHLSFhlBbkAu7HZZF2do2iL7SJIkSZIk2RpJB7kB2aPzIUmSJEmSJA1LOsgNyFWc3CX7jB8nSZIkjYKkDUC1FFuT7TGSZgM72+4f4/pTFNqOC+nnf6Wo5IlSw3hQCH0Y+Intz4ddd4rC2wO2T5d0JbDa9vWVPSwD+tteHiIYn6UIX2wELrD9QMxzFaUKxSth+jPb18QcgyhVM7oBP7Y9Jtp3ByYDBwDLgM/YXhl9lwLDY62v2b472o+itYrFncDFti1pB+BW4ChKPechtpeFzTCKoArAaNsTo30SpSbx68CD8TyvS/om8D9ifHfgA8BewLtjjffG84+z/f2Y6zpKqbjXgP8AzrX9l46epT3SQW5ARt80s/NBHfHdL26ZjSRJkiTJ1k9HSnp7SzrV9oy69ouBF2z3A5B0CMUBhOK8Hi6ph+01wEnAnzZnI5KOoYhgHGl7naQ9aa11PJriNPazvTbqKF8Sdt0opc5OoghwzJM03fYSYCRwbzj9I+N+hKRDKWXaDqMo0N0j6eAo9TaWUl5uLsVBHkQp9TYcWGm7j6ShwLXAkHDCr6A4wgYejvVXUtTuPhfP8FNKSbuxtq8Drov9fwL4epSG2wG4xPbv4xkfljQrnmUWcGmUtbsWuHQznqVN0kFuQJ78u67ZZx3kJEmSJAGKA3cZrXWAa/SiIgxi+7G6/hnAx4GpFLnp2yny0Z3RC1hue13MuxwgItZfAg6wvTb6WoArw24AsLQmrSypCRhMqXE8mFaVu4kUhbsR0d4Uaz0VtY0HRDR7F9tzYq5bgU/FMw2urDmVIlwi4BRglu0VYTOL4lTfbvsNqW1JD7KpSl+N2jsihEqerz2jpEeB3sAS29UI4FyKWAntPQswp823TDrIDclB93RxgvSQkyRJkm0ESefTKrYB5Z/sx1Xu64VCvm17clzPAc6QdDzQUhkzHpgp6SzgXmCi7Scq/U3A5ZJ+RUnDGM/mOcgzw+5x4B5gsu3fUP7P/Ew4xW3RG3i2ct8MHB3X+4TTSchN712xmVtn05sSCW9uo32TdSKKu4py9Kmt9XtX7pH0LuDzlOh7tX0nijN9Uf1DSToA+FvggTae+YuU1JGOnqVd0kFuRF7tfEiSJEmSNALhDI/rYEhHKRZQUhsuo0Rda3POl3QQRVL5REpKwzG2H43+BeHcnU1JUdhkS+1v1asj//dY4HhgcqRF/L46UNK5FEdzD+DD0GYpu87Ks7Zn09Fcb8Wmxk3A/bZ/W9f+CeDfatHnNzYn7UxRJ/yftl+u6xtFUf+b1Mm+2iUd5AZk0Se7Zp8B5CRJkiQp2L4vZJQH1rWvBqYB0yRtpMgmP1oZMh24npLeUC0w9RIllaJKT8pBPyJvdjYwW9JCYBgwBdhfUk/bLbYnABMkLaIcymsG9qvMty/wXFy/IKlXRI97UaSt6cCmmU3TIKpz1Wya49DgrhTJ6WZa0zhqNrNrN5KuoBzAu4A3M5RIr6iMfxfFOZ5ke1pd3zBKnvYJbhX76Oj52yQd5Abk8O90cYKxW2QbSZIkSbKtcA3wI6CW4/sRSk7sSknbA4dScQiD8cAq2wslHVdpvx+YJGlM5NieCTxie0Mc9ttYSdc4Anja9quSbqbk/F4Qh/S60XqAbx7QV9KBlAOBQymVMKA46sOAMfH3jkr7TyV9l3KwrS/wYOyjRdJASmrDOcANdXPNoeT/3hfVLe4GviXpPTHuZMoBOiSdR8lRPsH2xuoLkrQr8FFaD/EROc03A4/a/m7d+EGUSP5HbVf/vbzNZ6ED0kFuQO66pGv2b0oCSpIkSZJtl/oc5Ltsj6wOsH2npBcrTe8DxoYztx3wa0rEs2rTTCm7Rl37Akk3Ar+LknB/plR2ANgZuEHSbpQUgqW05k+PAq4GFklqAdZQDt09F/nAFwF3UyLK420vDrsxwBRJw4FnKGXisL1Y0hTKQb71wIWVqg9fobXM2wxaDyneDNwWh+BWUBxxovrE1RRHHeCqSsrEjygHGueU18U021dF3xnATNu1snUAH6HkKi+sfC//EIf9bgR2AGbFXHNtf7mTZ2mTlJpuQJZ+ZWiXvvQ+Y5tSajpJkiTZJkip6aQtMoLciLRVQCVJkiRJkiQB0kFuTJ7ofEiSJEmSJEmjkg5yA/L8ZZ2P6YisYpEkSZIkybZMOsgNyA95V5fsN6eSeZIkSZIkyTuV7d7uDST/9/l/eL1LnyRJkiRpFCRtkDS/8hkZ7bMlPVQZ11/S7LjeSdIkSQslLZL0uxC2QJIl3Vax6y7pxVDVQ9KVkr5Rt4dlkvaM61GSFktaEPs5ujLPtyQ9UdnrqMocgyQ9Jmlp7RmifXdJs8JuVqUUG5IujfGPSTql0n5UPNtSST+Iah1I2kHS5Gh/IMRQajbDYo0nolZxrf0WSU9V9nxEtL9H0i/iOR+UdHjF5uvxDhZJul3SjpW+r8Z+F0v650r7ByXNifaFVZu2yAhyAzKow8ImSZIkSZJU6EhJb29Jp9qeUdd+MfCC7X4AUb+4FmF6BThcUg/ba4CTKLWJO0XSMRQRjCNtrwunuVbreDTwXqBf1EHuCVwSdt2AH8ZazRRlv+m2lwAjgXttjwnHeSQwQtKhlDJth1FqB98j6eAojzaWUl5uLkUJcBCl1NtwYKXtPpKGAtcCQyTtDlwB9Kco2D0c66+MvX/T9tS6x/0HYL7tMyS9P/Z/gqTewNeAQ22vifJtQ4FbVCS/BwMfjPezdzx/d+AnwOdtPyJpj8r30SbpIDci/6eL9odskV0kSZIkyTud6ygy0/UOci9KbV8AbD9W1z8D+DgwlSI3fTubl8HYC1hue13MuxxKxBr4EnCA7bXR1wJcGXYDgKW2a0ImTRRHckn8PS7GTaQImoyI9qZY66mobTxA0jJgF9tzYq5bgU/FMw2urDmVIlwiihDIrFrtY0mzKE71Jgp5dRwKfDue5Y+SDpC0T/R1p9Snfh3YiVZVvK8AYyrvp6YKeDKwwPYj0f5SB+u+sUDSYKw68e3eQZIkSZJsHUg6n1axDYBxtsdV7uuFQr5te3JczwHOiMhlS2XMeGCmpLOAe4GJFfU7gCbg8kir+GCM3xwHeWbYPQ7cA0y2/RvK+flnwilui97As5X7ZuDouN7H9vMAITe9d8Vmbp1Nb0rktbmN9k3WCXGSVRQZ7bbW7125v0bS5ZR3NTIc3EeAMymCKQOAvwb2tf2wpOspoiZrKEIiM2Oeg4FjJV0DrAW+YXtetNcU/faiOP7/TAekg9yA7HpTFyf4budDkiRJkuSdQDjD4zoY0lGKBZTUhssoUdfanPMlHUSJXJ5ISWk4xvaj0b8g8nPPpqQobLKl9rfq1ZKOojjTxwOTIy3i99WBks6lpHnsAXwY2hRD6Uzgqz2bjuZ6KzaXAv9JSRUZR3mPV1EU/r4fP04WAn8A1keO9GDgQOAvwM8kfc72Tyh+7XuAgcCHKAqBB0X7/xNtrwL3SnrY9r3tPXwe0mtE1nTxkyRJkiQJALbvA3akOGXV9tW2p9n+O0r+62l1ptOB63lzmsFLFCevSk+KM4jtDbZn274CuAj47xTJ6f0j7xjbE8KpX0WRlm4G9qvMty+taQkvSOoFEH9raQnt2TSzqeRYda43bCLvd1eK5HS769t+3oV1wARKOgi2X7Z9bjzHOZTI71OUHxxP2X7R9uvANMqPgNr602K+B4GNwJ7R/hvby22/SvlRciQdkA5yI9Kji58kSZIkSapcA/x97UbSR2rVICRtT8mnfbrOZjxwle2Fde33A5+sObuSzgQesb1B0iGS+lbGHgE8HU7fzZSc3x3DrhutB/jmAX0lHRj7GUpx0Im/taoSw4A7Ku1DozLFgUBf4MFIx2iRNDDyi8+ps6nNdRZwn20DdwMnR2WK91Ai63fHPmvOuSi5zIvifrfYK8B5wP22X6akVgxUqRQi4ATg0Rj3S+BjYX9wPP/yWOuDYdMd+Cgl/7pdMsWiETn47d5AkiRJkrxjqM9Bvsv2yOoA23dKerHS9D5gbDhw2wG/Bn5eZ9MMfL9+sUi/uJGSe2tKRPe86N4ZuEHSbsB6SuS4lj89CrgaWCSphfJvvhOB5yIf+CKKo9gNGG97cdiNoaQiDKc4n5+OfSyOChFLYq0Lo4IFlMNwt1DCZjNoPaR4M3BbHOhbQXHEsb1C0tUURx3KD4MVcT1J0l6UNIz5wJej/QPArZI2xB6Gx1wPSJpKSStZT0m9qKXIjAfGS1oEvAYMCwd9paTvxvoG7rT96/p3X0XFLmkkll4ztEtfep9RTW/KJVq7vtNcpiRJkiTZ6tixe5v5sUmDkw5yAzL0saVd+tKbDumTDnKSJEmyTZAOctIW6SA3IBlBTpIkSZJCOshJW2QOcgPy0yFds798y2wjSZIkSZJkqyQd5AZkCUe93VtIkiRJkiTZakkHuQH5Jg+/3VtIkiRJkncEUUWhWoqtyfYYSbOBnW33j3H9gettHxfSz/9KUckTpYbxoBD6MPAT258Pu+7A88ADtk+XdCWw2vb1lT0sA/rbXi5pFPBZYAOlzu8FUdmhO0Vg49PAK2H6M9vXxByDKFUzugE/tj0m2ncHJgMHAMuAz9heGX2XUqpHbAC+ZrtWmu0oWqtY3AlcbNuSdgBuBY6i1HMeYntZ2AyjCKoAjLY9MdpFEVv5dKwz1vYPohzceEpFkLXAF20vkrRfrPHeeP5xtr8fc11JkdyuVRT5h6gwcgClFFxN8nuu7Vq1jDZJB7kB2fXFzsd0SJ8tso0kSZIkeSfQkZLe3pJOtT2jrv1i4AXb/QAkHUKRaIbivB4uqYftNcBJwJ82ZyOSjgFOB460vU7SnrTWOh5NcRr72V4bdZQvCbtuwA9jrWaKst9020uAkcC94fSPjPsRkg6llGk7DPgr4B5JB0ept7GU8nJzKQ7yIEqpt+HAStt9JA0FrgWGhBN+BdCfUmbt4Vh/JfAFiojI+21vrEhd/wMw3/YZkt4f+z+BUtrtEtu/j2d8WNKseBaA71V/XFT4j04UETchHeQGZNVeb/cOkiRJkmSb4DpKVLTeQe5FRRjE9mN1/TOAjwNTKXLTt1PkozujF7A8VOewvRwgItZfAg6wvTb6WoArw24AsNT2kzG+iSLXvCT+HhfjJgKzKXLPgynR8nXAU1HbeEBEs3exPSfmupUi8DEjbGprTqUIlwg4BZhVq30saRbFqb6dUlP5s7Y3xr5rSn6HAt+Otj9KOkDSPiFU8nztGSU9CvSmE+GP/yrpIDcg17FLl+ybttA+kiRJkuQdQL1QyLdtT47rOcAZko4HWipjxgMzJZ0F3AtMtP1Epb8JuFzSryhpGOPZPAd5Ztg9DtwDTLb9G8q/7T4TTnFb9Aaerdw3A0fHdc3pxPbzlQhub0qEuGrTmxIJb26jfZN1QpxkFbBHO+vXbN5HiTKfQUmN+Fq8q0eAMymCKQOAv6ZIVL9QmyRSJ/4WeKAy90WSzgEeokSaV0b7gZL+ALwMXGb7t22/qkI6yA3IN3n57d5CkiRJkmwVSDqfVjU6KDmt4yr3HaVYQEltuIwSdQXA9nxJB1EklU+kpDQcY/vR6F8Qzt3ZlBSFKu2VTXXkMB9FcaaPByZHWsTv657pXEqaxx7Ah6HNUnadlWdtz6ajud6KzQ7AWtv9Q1a79mNhDPD9+HGykKKYt/6NzUk7U9QJ/2dIUENJ/bg65r4a+A7wRUrEeX/bL8X7+6Wkwyp2byId5Abk2c6HdEjWwEiSJEm2FcIZHtfpwPbt7wsZ5YF17auBacA0SRuB0ygHxWpMB66npDfsUWl/iZJKUaUn5aAfkQM8G5gtaSEwDJgC7C+pp+0W2xOACSG53I0Ssd2vMt++wHNx/YKkXhE97kWRtqYDm+a4bmuumk1zHBrclSI53UxrGkfNZnbFpibD/QtgQjzny8C58MZBvqfig6R3hc0k29Nqk9quRpf/FfhVtK8DamkpD0v6D+BgSpS5TdJBbkAOv6eLE+QhvSRJkiSpcg3wI6CW4/sRYIntlZK2p+TTzq6zGQ+ssr1Q0nGV9vuBSZLGRI7tmcAjtjfEYb+NlXSNI4Cnbb8q6WZKzu8FcUivG60H+OYBfSUdSDkQOJRSCQOKoz6MErEdBtxRaf+ppO9SDun1BR6MfbRIGkhJbTgHuKFurjnAWcB9Ud3ibuBbUZkCSmT90rj+JfCxeB8fBR6Pd7gb8Krt14DzgPttvxzO8s3Ao7a/W32hNUc/bs8AFkX7XsCK2PtB8SxP0gHpICdJkiRJkrRPfQ7yXbZHVgdEKbFqjaj3AWPDmdsO+DWtUdKaTTOl7Bp17Qsk3UjJvTUlontedO8M3BDO43pgKa3pIaMoaQWLJLUAayiH7p6LfOCLgLspEeXxtheH3RhgiqThwDOUcmvYXixpCuXw23rgwoheQzlYdwulzNsMWg8p3gzcFgf6VlAccWyviCj7vBh3Ve3AXqw/SdLXgdWVZ/0AcGuU2VtCqZAB8BHg88DCyvfyD7bvBP5Z0hGUFItlwAXR/9+AqyStp5SS+3Jl/TZJqekGZOjS87v0pTf1GZdS00mSJMk2QUpNJ22REeQG5PI8pJckSZIkSdIu6SA3INvf1MUJvtv5kCRJkiRJkncq6SA3Imve7g0kSZIkSZJsvaSD3Ij8zdu9gSRJkiRJkq2X7d7uDSRJkiRJkiTJ1kQ6yI3IHl38JEmSJEmDIGmDpPmVz8hony3pocq4/pJmx/VOkiZJWihpkaTfhfIbkizptopdd0kvhuw0kq6U9I26PSyTtGdcj5K0WNKC2M/RlXm+JemJyl5HVeYYJOkxSUtrzxDtu0uaFXazKrWKkXRpjH9M0imV9qPi2ZZK+kGUs0PSDpImR/sDoRZYsxkWazwhaVil/aIY79ozRvtxklZVnuXySt9ukqZK+qOkRyUdU3l3f6rYnBbt20uaEHt+pK7udJtkikUDMu9vu2afOiFJkiRJA9GR1PTekk61PaOu/WLgBdv9AELg4/XoewU4XFIP22uAkyjiHZ0SjuDpwJG214VDWRMDGQ28F+gXQiE9gUvCrhvww1irmSJ9Pd32EmAkcK/tMeE4jwRGSDqUUsf4MIpQyD2SDo5ayGMp9ZfnUqSyB1FqIQ8HVtruI2kocC0wRNLuwBVAf0qN4odj/ZXAv1EU72a38ci/tX16G+3fp9SjPktFiGWnSt/3bF9fN/5LALb7SdobmCHpQ7Y3tveu00FuQD70nS5OMHaLbCNJkiRJ3ulcB1xGq1BGjV7A07Ub24/V9c8APg5MBc4GbgeO3Yz1egHLQzoZ28uhRKwpTuABttdGXwtwZdgNAJbarin9NQGDKQIcg2mVgZ5IcVRHRHtTrPVUiH8MkLQM2MX2nJjrVuBT8UyDK2tOpSj7CTgFmFUT55A0i+JU3277D9G2GY8PknahCH98IZ7zNeC1TswOBe6N8X+W9BeKs/5gewaZYtGI9OjiJ0mSJEm2ESSdL+mhyuf8uiE96lIshlT65gDrJB1fZzOeEoWdI2m0pL51/U3AUEk7Ah+kSDZvDjOB/SQ9LukmSR+N9j7AM+EUt0Vv4NnKfXO0AexTk2eOv3t3YtM7rtua6w0b2+uBVZTkzI7W74hjIiVihqTDou0g4EVggqQ/SPqxpHdXbC6K9JPxlXSRR4DBkYZyIHAUsF9HC6eD3Iis6eInSZIkSbYRbI+z3b/yGVc3ZI3tIyqfyXX9oylR5Oqc8ymO3HXA7pSUhg9U+hcAB1Cix3fWb6n9rXo1xbk7n+IkTpb0hfqBks4NZ/5ZSftBm2qBnSngtmfT0VxvxaY9fg/8te2/AW4Afhnt3YEjgbG2/5aSslLLqR5Lkfk+AngeqP2b+XiKU/4Q8C/Av1Pks9slUywakXPe7g0kSZIkybaB7fskXQ0MrGtfDUwDpknaCJwGPFoZMh24npLeUD0C/xIllaJKT+AvMe8GShrEbEkLgWHAFGB/ST1tt9ieQImwLgK6UZzDasR0X+C5uH5BUi/bz0vqBfw52tuzaY7rtuaq2TRL6g7sCqyI9uPqbGbTAbZfrlzfGRHzPWOuZtu1qPtUwkG2/ULNRtK/UnKba9Hsr1f6/h14oqP100FuRG7tov0xW2QXSZIkSbKtcA3wI6CW4/sRYIntlXGI7FDe7BCOB1bZXlhXVeF+YJKkMbZbJJ0JPGJ7Qxz222i75twdATxt+1VJN1Nyfi+IQ3rdaD3ANw/oG+kFf6Icvvts9E2nONlj4u8dlfafSvou5ZBeX+DB2EeLpIGU1JBzKBHe6lxzgLOA+2xb0t3AtyopDycDl3b0QiW9l3LQ0ZIGULIeXor7ZyUdErndJ1Byqak5+jHFGcCiaN8JkO1XJJ0ErI8Diu2SDnID8vwlXbPPKhZJkiRJA9FD0vzK/V22R1YHRITzxUrT+4CxcUBtO+DXwM/rbJop1Rioa18g6Ubgd5JMieieF907AzdI2o2SIrCUkm4BMAq4GlgkqYWSFDkReM72ekkXAXdTIsrjbS8OuzHAFEnDgWeAT8c+FkuaQnE+1wMXRvQa4CvALZSTSTNoPaR4M3BbHOhbQXHEsb0iouzzYtxVlQN7XwP+nlKBY4GkO22fR3GwvyJpfTzLUNu1tIyvUn5EbE/5UXJutP+zpCMo6RvLgAuifW/g7ojk/wn4fP17r0etayWNwtCld3TpS2/qM/hNuURr13eaS5QkSZIkWx07dm8zPzZpcDKC3ICMvuf2rk3QZ/CW2UiSJEmSJMlWSDrIDciTJ3bNPlMskiRJkiTZlkkHuQE5aPXbvYMkSZIkSZKtl3SQG5H/3UX7VNJLkiRJkmQbJoVCkiRJkiRJ2kHShjolvZHRPlvSQ5Vx/SXNjuudJE2StFDSIkm/k7Rz9FnSbRW77pJelPSruL9S0jfq9rAsagAjaZSkxaEWN1/S0ZV5viXpicpeR1XmGCTpMUlLa88Q7btLmhV2syql2JB0aYx/TNIplfaj4tmWSvpBVOtA0g6SJkf7A5IOqNgMizWekDSs0j4p5l+kon73rmj/ZuU5FsX3sHv07SZpqqQ/SnpU0jHRPrlis6xWfUTS/6j7DjdGtYt2yQhyI5Jy0UmSJEmyuayxfUQ7fXtLOtX2jLr2iyk1fPsBRP3i16PvFeBwST1srwFOopQe65RwBE8HjrS9LpzmWq3j0ZRSaf2iDnJP4JKw6wb8MNZqpij7TY9awCOBe22PCcd5JEUm+1BKmbbDKHWQ75F0cJR6G0spLzeXogQ4iFLqbTiw0nYfSUOBa4Eh4dheAfSnlGB7ONZfCUwCPhfP8FNKSbuxtq+jKBEi6RPA12ul4Sjl8e6yfVaUetsJwPYbMuCSvkORusb2pFgHSf2AO0LtsF3SQW5Anv+7rtnnIb0kSZIkAYoDdxmtdYBr9AKert2EoEWVGcDHKSpwZwO3A8duxnq9gOW218W8y+ENIYwvAQfYXht9LcCVYTcAWGq7JmTSBAym1DgeTKvK3USKoMmIaG+KtZ6K2sYDJC0DdrE9J+a6FfhUPNPgyppTKcIlAk4BZlVqH8+iONW3235DalvSg2yq0lej9o6QtAvw34AvxHO+BrxWHRxrfgb4WEdzdUSmWCRJkiRJ0rBIOl/SQ5XP+XVDetT98/yQSt8cYJ2k4+tsxlOisHMkjZbUt66/CRgqaUfggxRFus1hJrCfpMdVpJc/Gu19gGfCKW6L3sCzlfvmaAPYp6Y+F3/37sSmd1y3NdcbNiHvvIoio93R+gBEasXngbvq2neiONM1oZWDgBcpUtp/kPRjSe+ue95jKRH8tuSkh5AOctIWO3XxkyRJkiTbCrbH2e5f+YyrG7LG9hGVz+S6/tGUKHJ1zvkUR+46YHdKSsMHKv0LgAMo0cw72ZT2hLdsezVwFCW94UVgsqQv1A+UdG44889K2g/aFEPpTOCrPZuO5norNjVuAu63/du69k8A/1ZJr+gOHElJw/hbSsrKyDqbNqPEka/9qu1FbexnEzLFogH5WRftj9oiu0iSJEmSdz6271ORUR5Y174amAZMU5E4Pg14tDJkOnA9Jb1hj0r7S5RUiio9gb/EvBsoaRCzJS0EhgFTgP0l9bTdYnsCJcK6iCIt3QzsV5lvX+C5uH5BUi/bz0vqRZG2pgObZjZNg6jOVbNpltQd2JUiOd1MaxpHzWZ27UbSFcBetEpDVxnKps5uM9BsuxZ1n0rFQY51z6Rtd6V+rnZJB7kBOa/+t+9/lVGdD0mSJEmSBuIa4EdALcf3I8AS2yvjENmhVBzCYDywyvZCScdV2u8HJkkaY7tF0pnAI7Y3xGG/jZXUgSOAp22/KulmSs7vBXFIrxutB/jmAX0lHUg5EDgU+Gz0Tac42WPi7x2V9p9K+i7lkF5f4MHYR4ukgZTUkHOAG+rmmgOcBdxn25LuBr6l1goZJwOXxrs6j5KjfILtjdUXJGlX4KO0HuLD9n9GZPyQyO0+gZJLXeNE4I+2m+vm2g74NCV/uVPSQW5AnhzS+ZiOyEN6SZIkSQPRo1YuLLjL9ib/pG/7TkkvVpreB4yNw2LbAb+mNYe2ZtNMqcZAXfsCSTcCv5NkSkT3vOjeGbhB0m7AemApJd0CSvjqamCRpBZgDeXQ3XO210u6CLibElEeb3tx2I0BpkgaDjxDcSKxvVjSFIrzuR64MKLXAF8BbqHUxZpB6yHFm4Hb4kDfCoojju0VEWWfF+OuqqRM/IhyoHFOeV1Ms31V9J0BzLT9St1r+irlR8T2lB8l51b62osS/zdK5PnJNvrehOzOUlCSbY2ZS4d26Us/uU/Tm3KJ1q7vNJcpSZIkSbY6duzeZn5s0uBkBLkBOShTLJIkSZIkSdolHeQG5K4uplhctGW2kSRJkiRJslWSDnIDMug7XZxg7BbZRpIkSZIkyVZJ1kFOkiRJkiRJkgoZQW5E2qoymCRJkiTJm5C0AVhYaWqyPUbSbGBn2/1jXH/getvHhfrbv1JU8kSpYTzI9uqoTPET258Pu+7A88ADtk+XdCWw2vb1lT0sA/rbXi5pFKVE2wZgI3CB7QdinqsoVShqVR9+ZvuamGMQpWpGN+DHtsdE++7AZIpwyTLgM7ZXRt+lwPBY62u27472o2itYnEncHGUc9sBuJVSg/glYIjtZWEzjFZBldG2J0b7JKA/8DrwYDzP61Hi7SfA/hR/9fqo70xU8fgxcDhFcOSLtudEpYzB8V7+DHzB9nNR7eJ/xzobY7+z3/xtt5IOcgPy5M5ds88yb0mSJEkDscb2Ee307S3pVNsz6tovpkgd9wOI+sWvR98rwOGSetheA5xEqU3cKZKOAU4HjrS9TtKetNY6Hg28F+gXdZB7ApeEXTfgh7FWM0XZb7rtJRSRjXvD6R8Z9yMkHUopmXYYpQ7yPZIOjlJvYynl5eZSHORBlFJvw4GVtvtIGgpcCwwJJ/wKioNq4OFYfyUwidY6xz+llLQbC1xIqSX9CUl7AY9JmmT7NYqjf5fts8L5rQn9Xmf7H+OZvwZcDnwZ+BKA7X6S9gZmSPpQfd3lKukgNyDjeVeX7E/eQvtIkiRJknc411GiovUOci9KbV8AQtCiygzg4xQVuJos8rGbsV4vYLntdTHvcoCIWH8JOMD22uhrAa4MuwHA0loNYElNlEjrkvh7XIybSBE0GRHtTbHWU1HbeEBEs3exPSfmuhX4VDzT4MqaUynCJaIIgcyq1T6WNIviVN9u+w2pbUkP0qrSZ6Bn2O9Mqau8XtIulJrGX4jnfA14La5frryrd9MqZ30ocG+M+bOkv1Cc9Qfbe9HpIDcgoye/3vmgjsgyb0mSJMk2gqTzaRXbABhne1zlvl4o5Nu2awVT5wBnSDoeaKmMGQ/MlHQWxTGbWFG/A2gCLpf0K0oaxng2z0GeGXaPA/cAk23/hvKPu8+EU9wWvYFnK/fNwNFxvY/t5wFCbnrvis3cOpvelEh4cxvtm6wT4iSrKDLaba3fu3KPpHcBn6dE3wFupCjzPUeR2h5ie6Okg4AXKVLafwM8TEmZeCXmuYai7rcKOD7megQYHD8M9qOkgOxHOshJlSzzliRJkiSFcIbHdTCkoxQLKKkNl1GirrU554cjdzJF+niepGNsPxr9CyQdQIke31k3X3vCW44c5qMozvTxwORIi/h9daCkcymO5h7Ah6FNMZTOBL7as+lorrdiU+Mm4H7bv437U4D5wMcoyoSzJP2W4rseCXw1cq+/T0kL+UcA26OAUZE/fREltWM88AHgIUpk/98p6oDtkg5yAzLoni5OkEnISZIkSQKA7fvicNjAuvbVwDRgmqSNwGnAo5Uh04HrKekNe1TaX6KkUlTpSTnoR+QAzwZmS1oIDAOmAPtL6mm7JQ6zTZC0iHIor5kSMa2xLyUyC/CCpF4RPe5FOdxGBzbNtKZB1M9Vs2mOQ4O7UlIjmmlN46jZzK7dSLoC2ItNywicC4xxkXxeKukp4P0UOexm2w/EuKkUB7men1Ikvq+wvR74emW9fweeaMPmDbLMWyOyRxc/SZIkSZJUuQb4+9qNpI9Iek9cb0/JgX26zmY8cJXthXXt9wOfjEN2SDoTeMT2BkmHSOpbGXsE8LTtV4GbKTm/O4ZdN1oP8M0D+ko6MPYzlOKgE3+HxfUw4I5K+1BJO0g6EOgLPBjpGC2SBkZ+8Dl1NrW5zgLuCwf3buBkSe+J93JytCHpPEq0+Oy6Q3PPACfEmH2AQ4Anbf8n8GwcfCTGLIlx1XfzSeCP0b6TpHfH9UnA+jig2C4ZQW5A5v1t1+wzgJwkSZI0EPU5yHfZ3iRiaftOSS9Wmt4HjA0HcjtKJPPndTbNlGoM1LUvkHQj8LsoCfdnSmUHKIfVbogyZ+uBpbTmT48CrgYWSWoB1lAO3T0X+cAXUZzSbsB424vDbgwwRdJwilP66djHYklTKM7neuDCiF4DfIXWMm8zaD2keDNwWxzoW0FxxLG9IqLs82LcVbUDe8CPKD8e5pTXxTTbV8Wz3BJRcgEjaocSga8Ck8LZf5ISbQYYE47zxpjzy9G+N3B3RPL/RMl17hAVxz5pJJZeM7RLX3qfUU1vyiVau77TXKYkSZIk2erYsXub+bFJg5MR5EYk0ySSJEmSJEnaJR3kRmRg50OSJEmSJEkalTyklyRJkiRJkiQVMoLcgKTUdJIkSZIkSftkBDlJkiRJkqQdJG2QNL/yGRntsyU9VBnXX9LsuN5J0iRJCyUtkvQ7STtHnyXdVrHrLunFUNVD0pWSvlG3h2WS9ozrUZIWS1oQ+zm6Ms+3JD1R2euoyhyDJD0maWntGaJ9d0mzwm5WrTxd9F0a4x+TdEql/ah4tqWSfhDVOoiScJOj/YEQQ6nZDIs1npA0rNJ+YIx9Imy3r/QdF8+xWNJvom1HSQ9KeiTa/6ky/m8kzYm9/b8qstRIOknSw9H+sKSPdfa9p4PcgLzUxU+SJEmSNBBrbB9R+Yyp9O0t6dQ2bC4GXrDdz/bhwHCKRDPAK8DhknrE/UmU0mOdIukY4HTgSNsfpKj01SScRwN/BfQL5b9jgXeFXTfgh8CplJrMZ0s6NOxGAvfa7kuRxa79ADiUUqbtMGAQcFPMAzCWUl6ub3wGRftwYKXtPsD3gGtjrt0pinZHAwOAKyqO+LXA92L9lTEHUcruJuCTtg8jys8B64CP2f4bSh3oQZJqp6t+DIy03Q/4BfDNaF8OfCLahwFv/EBpj0yxaEA+tKHzMUmSJEmSdMp1FJnpGXXtvagIg9h+rK5/BvBxigrc2cDtFIe2M3oBy22vi3mXQ4lYA18CDrC9NvpagCvDbgCw1PaTMb4JGEypcTyYVpW7iRSFuxHR3hRrPRW1jQdIWgbsYntOzHUr8Kl4psGVNadShEtEEQKZVat9LGkWxbFtokhJf7ay/pUUB/yzlJrIz8Tz/Dn+Glgd498Vn1qp2UMoQisAsyh1n//R9h8q73AxsKOkHWrvsS0ygtyINHfxkyRJkiTbCJLOl/RQ5XN+3ZAedSkWQyp9c4B1ko6vsxkPjIh/7h+tTRXeAJooKnU7Ah8EHmDzmAnsJ+lxSTdJ+mi09wGeCae4LXrTGmmG8n/z3nG9T6jjEX/37sSmN5t6A9W53rAJeedVlOKy7c21B/CXGFs/18HAeyKV5WFJ59SMJXVTEW/5M8Xxrr2/RRQFPSgR56pUdo3/DvyhI+cYMoLcmKx5uzeQJEmSJFsHtscB4zoYsiZSFtpjNCWKPKIy53xJB1EklU8E5kk6xvaj0b8g8nPPBu6s31L7W/VqSUdRos3HA5Mjn/j31YGSzqWkeewBfBjaFEPpTOCrPZuO5vqv2nQ0V3fgKIqUdA+K0t5c24+Hot8RkYbxC0mH214EfBH4gaTLKbLXr23yQNJhlJSOk9tYdxPSQW5EpnbR/vQtsoskSZIkecdj+z4VGeWBde2rgWnANBWJ49OARytDpgPXU9IbqhJeL1FSKar0BP4S826gpEHMVpFhHgZMAfaX1NN2i+0JwARJiyjS0s1sGk3dF3gurl+Q1Mv285J6UaKydGDTHNdtzVWzaZbUHdiVIjndTGsaR81mNiU3eDdJ3SOKXD/XctuvAK9Iuh/4G+Dx2iS2/6JyMHIQsMj2HwnnV9LBlDQW4n5fSl7yObb/g07IFIskSZIkSZKucQ3w97UbSR+pHUKLqgyHUslJDsYDV9leWNd+P/BJST3D/kzgEdsbJB1Sl65xBPC07VeBmyk5vzuGXTegVhFiHtA3KkZsTzl8Nz36plOcbOLvHZX2oVGZ4kDKYbwHIw2jRdLAyC8+p86mNtdZwH2RM3w3cLKk98R7ORm4O/r+T4ytX/8O4FiV6hw7UQ74PSppr4gcEwcdTwT+GPd7x9/tKFH9H8X9bsCvgUtt/xubQUaQG5AnL+uafdZBTpIkSRqIHpHvWuMu2yOrA2zfKenFStP7gLHhQG5Hcc5+XmfTDHy/frFIv7gR+J0kUyK650X3zsAN4fCtB5ZSqkkAjAKuBhZJaqEkVE4EnrO9XtJFFEe1GzDe9uKwGwNMkTQceIaoFmF7saQplIN864ELI3oN8BXgFkrqwwxaDyneDNwWB/pWUBxxbK+IKPu8GHdV7cAeJTWlSdJo4A8xB7YflXQXsADYCPzY9iJJHwQmxg+A7YAptn8Vc50t6cK4ngZMiOuLKO7LP0r6x2g7uXbwry1UnPekkVg6Z2iXvvQ+xzS9KWdo7fpOc5mSJEmSZKtjx+5t5sEmDU5GkBuQy/Ya3CX7pi20jyRJkiRJkq2RdJAbkNEv3tH5oI7oc/aW2UiSJEmSJMlWSDrIDchle+3SJfuMICdJkiRJsi2TDnIDMvrFl7s2QZ7SS5IkSZJkGybLvDUij3TxkyRJkiQNgqQNdUp6I6N9tqSHKuP6R01eJO0kaZKkhZIWSfqdpJ2jz5Juq9h1l/SipF/F/ZWSvlG3h2WS9ozrUZIWS1oQ+zm6Ms+3JD1R2euoyhyDJD0maWntGaJ9d0mzwm5WrTxd9F0a4x+TdEql/ah4tqWSfhDVOoiScJOj/YEQQ6nZDIs1npA0rNI+KeZfJGm8pHdF+66S/l9Jj8TzntuVZ4n+/SWtrn+/bZER5EZkYOdDkiRJkiQBOlbS21vSqbZn1LVfDLxgux+ApEOA16PvFeBwST1srwFOAv60ORuRdAxFrutI2+vCaa7VOh4NvBfoZ3utSh3lS8KuG/DDWKuZouw33fYSYCRwr+0x4WyOpMhkH0op03YY8FfAPZIOjlJvYynl5eZSlAAHUUq9DQdW2u4jaShFtW6IpN2BK4D+FKW8h2P9lcAk4HPxDD+llLQbC1wILLH9CUl7AY9JmgRs+K8+S+UVfo/WknQdkhHkRmRNFz9JkiRJkgBcRxGkqKcXFafX9mO211X6Z9Cq8nY2cPtmrteLoi63LuZdbvs5FSGNLwFftb02+lpsXxl2A4Cltp+0/RrlOFGtpNVgSr1k4u+nKu1NttfZfopSc3mAitreLrbnhNDHrXU2tbmmAidEdPkUYJbtFeEUz6I41di+0wHwIK0qfQZ6hv3OlLrK69/isyDpU8CTQK3+c4ekg9yI9OjiJ0mSJEm2ESSdL+mhyuf8uiE96lIshlT65gDrJB1fZzOeEoWdI2m0NlW/g+LUDVVRvfsg8MBmbncmsJ+kxyXdJOmj0d4HeMZ2Szt2vYFnK/fN0QawT6jjEX/37sSmd1y3NdcbNiEdvYoio93R+gBEasXngbui6UbgAxTp6YXAxbY3vpVnkfRuSiT5n+pfTHtkikUD8trOb/cOkiRJkmTrwPY4YFwHQzpKsYCS2nAZlX/Ktz1f0kEUSeUTKWkAx9h+NPoXRH7u2ZQUhU221P5WvVrSUcCxwPHA5Egl+H11YOTrXkxxTj8MbYqhdCbw1Z5NR3O9FZsaNwH32/5t3J8CzAc+RlEmnCXpt5s5Vz3/BHwv3l8nQwvpIDcgL73dG0iSJEmSbQTb96nIKA+sa19NkTueJmkjcBrwaGXIdOB64DiKI1vjJUoqRZWewF9i3g3AbGC2pIXAMGAKsL+knpFaMQGYIGkRRVq6GdivMt++lMgswAuSetl+PtInavLL7dk005oGUT9XzaZZUndgV0pqRHM8Z9Vmdu1G0hXAXsAFlTHnAmMi9WKppKeA97/FZzkaOEvSPwO7ARslrbV9I+2QDnID0mtyFycY1fmQJEmSJGkgrgF+RMlxRdJHKAfMVkraHjiUikMYjAdW2V4o6bhK+/3AJEljbLdIOhN4xPaGOOy30fYTMfYI4Gnbr0q6GbhR0gVxSK8brQf45gF9JR1IyY0eCnw2+qZTnOwx8feOSvtPJX2XckivL/Bg7KNF0kBKasg5wA11c80BzgLus21JdwPfqlSVOBm4NN7VeZRo8QmRQlHjGeAE4LeS9gEOiff7l//qs9g+tjappCuB1R05x5AOcmPyN2/3BpIkSZLkHUMPSfMr93fZHlkdYPtOSS9Wmt4HjI0DZtsBvwZ+XmfTDHy/frFIv7gR+J0kU6Kg50X3zsANknajHFhbSqkmASV8dTWwSFIL5Vj9ROA52+slXQTcTYkoj7ddO6w2BpgiaTjFKf107GOxpCnAkljrwoheA3wFuIVyMmkGrZUhbgZuk7SUEjkeGnOtiCj7vBh3le0Vcf0j4GlgTqQ/TLN9VTzLLRElFzDC9nKA/+qzvBVUItdJI7H0Z0O79KX3+XTTmxJ41q7vNP8nSZIkSbY6duzeZk5r0uBkBLkRefzt3kCSJEmSJMnWSzrIDchlQ07ukn3TFtpHkiRJkiTJ1kg6yA3IN5nZxRm+uEX2kSRJkiRJsjWSDnIDklofSZIkSZIk7ZMOcgOyfZZ5S5IkSZLNQtIGipJbjSbbYyTNBna23T/G9Qeut31cSD//K0UlT5TSZINCqMLAT2x/Puy6A88DD9g+vVKG7PrKHpYB/W0vlzSKUtZsA7ARuMD2AzHPVZTKDa+E6c9sXxNzDKJUzegG/Nj2mGjfHZgMHAAsAz4TctBIuhQYHmt9zfbd0X4UrVUs7qSo3FnSDhTp6aMo9ZyH2F4WNsNoleUebXtitE8C+gOvU6SmL7D9epSEG0+pCLIW+KLtRWHzdUplD8d3c26UtptMKQcHpd7xX2wfESp9PwaOpPi+t9r+dv13XSUd5AbksiHv6pJ95iAnSZIkDURHSnp7SzrV9oy69ouBF2z3A4j6xa9H3yvA4ZJ62F4DnESp59spko4BTgeOtL1O0p601joeDbwX6BfOYk/gkrDrBvww1mqmKPtNt70EGAncG07/yLgfIelQSpm2wyh1kO+RdHCUehtLKS83l+IgD6KUehsOrLTdR9JQ4FpgSDjhV1AcYQMPx/orgUnA5+IZfkpxfMcC/wDMt32GpPfH/k+Q1Bv4GnCo7TVRim4ocIvtN2TAJX2HInUN5UfDDrb7xY+XJZJurznvbZEOcgMy+I3/RpMkSZIk6QLXUaKi9Q5yL0ptXwBsP1bXPwP4ODCVIjd9O0U+ujN6Acttr4t5a3WBdwK+BBxge230tQBXht0AYKntmpBJEzCYUuN4MK0qdxMpgiYjor0p1noqahsPiGj2LrbnxFy3Ap+KZxpcWXMqRbhEFCGQWbXax5JmUZzq222/IbUt6UFaVfoOBb4dz/JHSQeEYAgU/7WHpNeBnWhV0qvNI+AzFJlqKE75uyPK3gN4DXi5oxedDnID8qE/dnGCPltkG0mSJEnytiPpfFrFNgDG2R5Xua8XCvm27Vqy4hzgDEnHAy2VMeOBmZLOAu4FJlbU76D8Y+zlkn5FScMYz+Y5yDPD7nHgHmCy7d9Q/s/8TDjFbdEbeLZy30yRXwbYx/bzACHRvHfFZm6dTW9KJLy5jfZN1glxklUUGe221u9duSfSID5Pib4DPAKcSRFMGQD8NbCv7YclXU8RAlkDzLRdX33gWEoEv/bOp1Kc9+cpDvXXK0IlbbJdR53JNsqaLn6SJEmSZBvB9jjb/SufcXVD1tg+ovKpP8kzmtbc2tqc84GDKBHm3SkpDR+o9C+g5PyeTUlR2MS8/a16NSW/93zgRWCypC/UD5R0rqT5kp6VtB+0KYbSmcBXezYdzfVWbGrcBNxv+7dxPwZ4T/w4+SrwB2B95CYPBg6kpH68W9Ln6uaqReVrDKDkUf9V2F0i6aA29vQGGUFuROZ00f4tCzcmSZIkybaF7ftCRnlgXftqYBowTdJG4DTg0cqQ6cD1lPSGPSrtL1FSKar0pBz0I3KAZwOzQ4Z5GDAF2F9ST9stticAEyQtohzKawb2q8y3L61pCS9I6hXR414UaWs6sGmmNQ2ifq6aTXOkM+xKkZxupjWNo2Yzu3Yj6QpgL+CCWpvtl4Fzo1/AU/E5BXjK9ovRNw34MPCTuO9OiTwfVVnvsxSJ8NeBP0v6N0o+9JO0QzrIDchlf/fNLtnnIb0kSZIk2YRrgB8RDpekjwBLbK+UtD0ln3Z2nc14YJXthZKOq7TfD0ySNMZ2i6QzgUdsb4jDfhsrqQNHAE/bflXSzZSc3wvikF43Wg/wzQP6SjqQciBwKMVphOKoD6NEbIcBd1Tafyrpu5TIa1/gwdhHi6SBwAPAOcANdXPNAc4C7ovqFncD34roL8DJwKXxrs6jOL0n2N5YewmSdgNetf0a5eDe/bZflvQMMDDyrtcAJwAPVd7ficAfbVfTQJ4BPibpJ5QUi4HAv9AB6SA3IF/kui7OkC5ykiRJ0jDU5yDfZXtkdYDtOyW9WGl6HzA2Ip/bAb8Gfl5n00wpu0Zd+wJJN1Jyb02J6J4X3TsDN4TzuB5YSmv+9CjgamCRpBaK8zgReC7ygS8C7qZElMfbXhx2Y4ApkoZTHMlPxz4WR4WIJbHWhRG9BvgKrWXeZtB6SPFm4LY40LeC4ohje0VE2efFuKsqOcA/ohxonFNeF9NsXwV8ALhVpczeEkqFDKKk3VTg97GvPwDVtJihbJpeAaUCxgRgESXdY0KkubSL7M5SUJJtjTFDl3bpSx/Z1OdNuURr13eay5QkSZIkWx07dm8zPzZpcDKC3ICc9Z7LOh/UIRlBTpIkSZJk2yUd5Ebkgs6HJEmSJEmSNCrpIDciWaotSZIkSZKkXdJBbkDm7dU1+9QJSZIkSZJkWyYd5AbkQ6O7OMEtW2IXSZIkSbL1E1UUFlaammyPkTQb2Nl2/xjXH7je9nFRguxfKSp5otQwHmR7dVSm+Intz4ddd4rC2wO2T5d0JbDa9vWVPSwD+tteLmkUpUTbBmAjcEFUdugOXEWpQvFKmP7M9jUxxyBK1YxuwI9tj4n23YHJFOGSZcBnbK+Mvksp1SM2AF+zfXe0H0VrFYs7gYujnNsOwK2UGsQvAUNsLwubYbQKqoy2PTHab6bUJBbwOPCFeE+K/Z4GvBrtvw/hk1uB98bzj7P9/ZhrMnBIrLEb8BfbR0g6iVKtY3uKzPQ3bd9X/11XSQe5AVnV1TN6SZIkSdI4rLF9RDt9e0s61faMuvaLKVLH/QCifvHr0fcKcLikHrbXACdRahN3iqRjgNOBI22vk7QnrbWOR1Ocxn5RB7kncEnYdaOUOjuJItoxT9J020uAkcC94fSPjPsRkg6llEw7jFIH+R5JB0ept7GU8nJzKQ7yIEqpt+HAStt9JA0FrgWGhBN+BcURNvBwrL+SIvv8cuzzu8BFFGf2VErt5b4UWeyx8Xc9cEk4yz1jrlm2l9geUnlX3wFWxe1y4BO2n5N0OKXc3SZS1/Wkg9yA7JoR5CRJkiTZElxHiYrWO8i9KLV9AbD9WF3/DODjwFRaZZGP3Yz1egHLba+LeZcDRMT6S8ABttdGXwtwZdgNAJbargmZNFHkmpfE3+Ni3ESKoMmIaG+KtZ6K2sYDIpq9i+05MdetwKfimQZX1pxKES4RRQhkVq32saRZFKf69opzLEpEulY2djBwq0s94rmSdqsp/lEi7oSQyqMUZ3dJ7SXFXJ8BPhbj/lB5h4uBHSXtUHuPbbFdex3JNkyPLn6SJEmSpHHoIWl+5TOk0jcHWCfp+Dqb8ZQo7BxJoyX1retvAoZK2pGShvHAZu5lJrCfpMcl3STpo9HeB3gmnOK26A08W7lvpjWCuk84ncTfvTux6R3Xbc31ho3t9ZQI7h6drI+kCcB/Au+nVZWvQ5uwOwD4W978/o6lRPCf4M38d+APHTnHkBHkxmTfzockSZIkSSMg6Xxa1eig5LRWldk6SrGAktpwGSXqCoDt+ZIOokgqn0hJaTjG9qPRvyCcu7MpKQr/X3vnHm1VdaX53ydExYCaYJlQPuIDtMqoZaulqJWHGYZSY4WKbRQ7Q2mD0XaEjunOC0srYSgkpIKmutWYJnLxESMQJaWtIr5CjJYPHMpLiYaUaN1oq/ggFxUUmP3HWid3czj3HOTc1N2X/f3GWOOevfa31p77LIbOu+5ccxbpqfBW5Njcw0gO4LHArBwW8XjdO51FCvMYChwNDYuhtCrw1dOYZnNtyRgi4qwcBnI5cBqp6l3TMZIGk6oTfrW2C12gtiu/EZI+Sgr7GNVg7o2wg1xFXu1rA4wxxphykJ3haS2FPY+/L5dRHlnXvxqYA8yRtIF02GxZQXIrMJUU3jC00P8qKZSiyBDSQT9yDPB8YL6kJcBYYDawp6QhEdEVETOAGZKWkg7ldQJ7FObbHXghf36pFrogaRiptDVNxnSy8VZbca7amM58aHAnUsnpTrrDOGpj5hdfMCLW50N23yA5yD3aLOl9JOf4hoiYU5wnP/dk0kHBYv/uwC+AMyPid7TAIRZV5Kg2mzHGGGOKTAa+WbuQdIykD+TP2wIHUIhJznQAF0fEkrr++4HP5gNoSDoZWJQdyP3rwjUOAZ6LiLeA6aSY3+3zuAF0H+BbAIyQtHe2ZwzJQSf/HJs/jwVuKfSPkbSdpL1Jh+UezWEYXZJG5ljfM+vG1OY6BbgvxxDPA0ZJ+kD+XkYB85QYnu0V8HfAbwpznZk1I4FV2YlXftdlEXEZm3Ic8JuI+GMYiKSdgduBCyLiwQZjNsE7yMYYY4wxPTNI0sLC9Z0RMaEoiIg7JL1S6NoXuCo7c9uQnLOb68Z0ktKYUde/WNIVwANKKeFeBs7OtwcDl2eHbx2wnO7wkAuBS4ClkrpIZcGuBV6IiHWSxpMc1QFAR0Q8mcdNAWZLGgc8T0oTR0Q8KWk26fDbOuDLefca4Dy607zNpfuQ4nTg+nyg7zWSI05EvJZ32Rdk3cW5bxvgWkk7kkIqFuW5IYWenJjf8S3grNx/DHAGsKSwLv8QEbVQlTFsGl4xnhSn/Y+S/jH3jYqIl+kBJcfeVIm7lo9pa9FHDZ+5SVzQmnUtY5mMMcaY0rH9wIaxrqbieAe5ggxtLTHGGGOMqSzeQa4g48Ysb2vRp88c7h1kY4wxWwXeQTaN8A5yBXlz0ottzjC8V+wwxhhjjCkjdpAryKQnrmxvguGbU+zHGGOMMaZ/Yge5irganjHGGGNMjzgPchXpbLMZY4wxFUHS+rpS0xNy/3xJjxV0h0uanz/vIOkGSUskLZX0QK78hqSQdH1h3EBJr0i6LV9PlPT1OhtWSNolf75Q0pOSFmd7jizM811Jvy3YemFhjuMlPS1pee0dcv8HJd2dx91dy9+c712Q9U9L+ttC/2H53ZZL+t85nR05Z/Ks3P+IUrXA2pg7Jb1Re89C/95Z+9s8dtvcrzz38vyuhxbGdEh6WakQSnGuiZJ+X3j/E3P/F+rWcIOkQ5qtu3eQq8iivjbAGGOM6Tc0KzW9q6QTImJuXf/5wEsRcRCApP2Bd/O9N4EDJQ2KiLeBTwO/3xxDJB0FnAQcGhFrs9NcKwYyCfgwcFBErFEqNPK1PG4AcGV+Viep9PWtEfEUMAG4NyKmZMd5AvAtSQeQcgp/FPhz4B5J++VcyFeR8i8/TMpXfDwpF/I44PWIGC5pDKms82nZvh8AOwDn1r3W94EfRsRMST/Oc1wFnEAqTjICODL3HZnHXANcAVzX4Gv6YURMLXZExA3ADfm7OAi4JSIW9vxN20GuJmf2tQHGGGPMVsEPgIvoLpRRYxiFynkR8XTd/bnAZ4CbgNNJhS0254DPMGBlRKzN866EtGMNfAnYKyLW5HtdwMQ87ghgeUT8W9bPBEaTioCMprsM9LWkEtDfyv0z87OezcU/jpC0AtgxIh7Kc10H/H1+p9GFZ95EquynSNwrqfYc8lgBnwL+S+H5E0nO8GjgulyJ72FJOyuXxI6I+4u70++R9JscTAAAF0hJREFU2vfdFIdYVBGHWBhjjDEASDpH0mOFdk6dZFDdn+dPK9x7CFgr6di6MR2kXdiHJE3SxuWhAWaSyjhvDxwMPLKZ5t4F7CHpGUk/kvSJ3D8ceD47xY3YDfj3wnVn7gP4UC4fTf65a4sxu7GxN1Cc649jImIdsIrm5ReGAm9kbY9zNbjXjPE5JKOjGC5S4DQ2w0H2DnIVub3N8Z/vFSuMMcaYPicipgHTmkiahVhACm24iLTrWptzoaR9gFHAcaSQhqMiYlm+vzjvgJ5OClHYyKSeTY3Vkg4j7TYfC8zKYRGPF4WSziKFeQwFjoaGuZ5b1S/oaUyzud7rc3pzLkg7z5dk3SXApcAX//iwFK/9VkQsbTy8GzvIFeSdi/raAmOMMWbrICLuk3QJMLKufzUwB5gjaQNwIrCsILkVmEoKbyjusr5KCqUoMgR4I8+7nhQGMV/SEmAsMBvYU9KQiOiKiBnAjHyIbQBp93WPwny7Ay/kzy/VQhckDQNezv09jenMnxvNVRvTKWkgsBPwGj2zEthZ0sC8i9xorkbPaUhEvFT7LOknwG11kjFsxu4xOMSikmy7ur1mjDHGmI2YDHyzdiHpmNqf93NWhgMoxCRnOoCLI2JJXf/9wGfzITsknQwsioj1kvavC9c4BHguIt4CppNifrfP4wbQfYBvATAiZ4zYluQo3prv3Upyssk/byn0j8mZKfYmHZZ7NIdhdEkamWOIz6wbU5vrFOC+aFKyOd/7ZdY2ev6ZOZvFSGBVLRSkJ7KDX+NzwNLCvW1IfwOf2WyOGt5BriK/bXP8Ib1hhDHGGNMvGCRpYeH6zoiYUBRExB2SXil07QtclR3IbUjBjTfXjekE/lf9w3L4xRXAA5KCtKN7dr49GLhc0s7AOmA5KZsEwIWksIKlkrqAt0mH3l6IiHWSxgPzSDvKHRHxZB43BZgtaRzwPDmQMiKelDSbdJBvHfDlvHsNcB4pk8Qg0uG82iHF6cD1+UDfayRHHABJvwb+AhgsqRMYFxHzSKEpMyVNAp7Ic0AKPTkxv+NbwFmFuW4k7bzvkuf6TkRMB/4pp28LYAUbZ8z4ONBZO6jYCjVx7M1WyvL/OaatRR9+2cxN4oLWrGsZF2SMMcaUju0HNox1NRXHO8hV5O2+NsAYY4wxprzYQa4gC77W3vjhvWOGMcYYY0wpsYNcQW7hsLbGn95LdhhjjDHGlBE7yJWkWcYVY4wxxphqYwe5gky69Nn2Jriqd+wwxhhjyo6k9UAxFdvMiJgiaT4wOCIOz7rDgakR8clc+vknpCp5IuUwPj4X+gjgpxFxRh43EHgReCQiTpI0EVgdEVMLNqwADo+IlZIuJJVmXg9sAM6NiEfyPBeTslC8mYf+PCIm5zmOJ2XNGABcHRFTcv8HgVnAXqTMD6dGxOv53gXAuPysr+SsE+RiJdeQsljcAZwfESFpO+A64DBSPufTImJFHjOWVFAFYFJEXJv7rwE+Qaq6B/Bfc6GV0aSsHBtIWTS+GhEP5DHnk0prC/hJRPxzs3eR9AXgG4U1PBg4NCIW0gPOg2yMMcYY0zNvR8QhhTalcG9XSSc0GHM+8FJEHBQRB5KczHfzvTeBAyUNytefBn6/OYZIOgo4ieTcHUyq0lcrxzwJ+HPgoFz572PA+/K4AcCVwAmknMynSzogj5sA3BsRI4B78zX5/hjgo8DxwI/yPJC2ys4h5UYeke+T3/P1iBgO/BD4fp7rg8B3gCOBI4Dv1JWB/kbh+12Y++4F/iq/yxeBq/NcB5Kc4yOAvwJOKuSGbvguEXFDbX7gDGBFM+cYvINcSd5p85CeMcYYYwD4AWlXdG5d/zAKhUEi4um6+3OBzwA3kY723EhyaFsxDFgZEWvzvCsB8o71l4C9ImJNvtcFTMzjjgCW13IAS5oJjCblOB5NyikMKW/yfFJu4tGk3fK1wLM5t/EReTd7x4h4KM91HfD3+Z1GF555E6lwiYC/Be6OiNfymLtJTnWPVe1yJcIa76e7zPRfAg/n4ihI+hWpKMg/NXmXIrXvuyl2kCvItk+0OYHTWBhjjNlKkHQO3cU2AKZFxLTCdX2hkO9FxKz8+SHgc5KOBboKmg7gLkmnkHYyr42IYpmumcC3Jd1G+nN/B5vnIN+Vxz0D3APMiohfkf7P/Hx2ihuxG907zZDKOB+ZP3+oVqEul5vetTDm4boxu5F2wjsb9G/0nFycZBWpjHaj5+9WuJ4s6dvkXd/aLwCSPgd8D9iV9AsFpOp4kyUNJSWuPRF4rMW7FDmN5Eg3xQ5yBXnxP7U33v6xMcaYrYXsDE9rInk7/2m+JyaRdpH/uFOZY2j3AUaRwiAWSDoqIpbl+4sl7UXazbyj3qSeTY3VOf73Y8CxwCxJE4DHi0JJZ5HCPIYCR0PDYiitCnz1NKbZXFsy5gLg/5HKYk8jfY8XA0TEL4BfSPo4KR75uIhYJun7wN3AamARKUa5JZKOBN6KiKWttHaQK8iwS9ucwIf0jDHGGAAi4j5JlwAj6/pXA3OAOZI2kHY6lxUktwJTSSEBQwv9r5JCKYoMIR30I5d7ng/Ml7QEGAvMBvaUNCQiuiJiBjBD0lLSobxOYI/CfLsDL+TPL0kalndch5FKW9NkTGf+3Giu2pjOfGhwJ1LqrE66Qx9qY+bn93kx962VNAP4et27ExH3S9pX0i4RsTKXlZ4OIOm7dO9o9/QuNcawGeEVYAe5krhQiDHGGNOrTAZ+DNRifI8BnsoZFLYlHYybXzemA1gVEUskfbLQfz9wg6QpEdEl6WRgUUSsl7Q/sKEQrnEI8FxEvCVpOinm99yIWJMP1G2bdQuAEZL2Jh0IHEPKhAHJUR8LTMk/byn0/0zSZaTDfyOAR7MdXZJGAo8AZwKX1831EHAKcF/ObjEP+G7hYN4o0s4xBYdWpFjmpbl/OPC7PP7Q/C6v5nu7RsTLkvYETgaOavEuSNqGlOHj42wGdpAryC3rJ7U13oVCjDHGVIj6GOQ7I2JCURARd0h6pdC1L3BVdvq2AW4Hbq4b00lKu0Zd/2JJVwAP5JRwLwNn59uDgcsl7UwKK1hOd/z0haQwhKWSukjxudcCL+R44PHAPNKOckdEPJnHTQFmSxoHPE9yIomIJyXNJh3kWwd8Oe9eA5xHd5q3uXQfUpwOXJ8P9L1GcsSJiNfyLvuCrLu4dmCP9MvAn5HCMBYC/y33/2fgTEnv5nc5LSJqYRk35xjkd7Ndrzd7l8zHgc7aQcVWqPtZpirctXxMW4s+avjMTWKJ1qxrGctkjDHGlI7tBzaMjzUVxzvIFaSDHdsaP6qX7DDGGGOMKSN2kCvIpEv/0N4EPqRnjDHGmK0YO8hVZPfWEmOMMcaYqmIHuYKsOq2vLTDGGGOMKS92kCvITs6DbIwxxmwWktYDSwpdMyNiiqT5wOCIODzrDgemRsQnc+nnn5Cq5ImUw/j4XOgjgJ9GxBl53EDgReCRiDhJ0kRgdURMLdiwAjg8IlZKupCUom09sAE4NyIeyfNcTMrc8GYe+vOImJznOJ6UNWMAcHVETMn9HwRmAXsBK4BTa1khJF0AjMvP+kpEzMv9h9GdxeIO4Pycjm074DrgMFJKttMiYkUecycpV/QDEXFS4d3GA18lZf74s0L57C/QXXxlNXBeRCzK9zqAk4CXI+LAuvX678B4UuaN2yPim5LeB1wNHEryfa+LiO/RBDvIVeTcvjbAGGOM6Tc0q6S3q6QTImJuXf/5wEsRcRBAzl/8br73JnCgpEER8TbwaVJu4pZIOorkGB4aEWsl7UJ3ruNJwIeBg3Ie5CHA1/K4AcCV+VmdpMp+t0bEU8AE4N7s9E/I19+SdAApTdtHSXmQ75G0X071dhUpvdzDJAf5eFKqt3HA6xExXNIY4Puk0s4APwB2YFMv5EHgNjbNE/0s8ImcS/oEUpW9Wnnsa4ArSM548fs5llRG+uD8/dRKTX8e2C4iDsq/vDwl6caa894IO8hV5P+0Od47yMYYYwwkp+8iuvMA1xgGPFe7iIin6+7PBT4D3EQqL3AjqXx0K4YBKyNibZ63ttu6A/AlYK+IWJPvdQET87gjgOW1HMCSZpIcyafyz09m3bUkR/VbuX9mftazObfxEXk3e8eIeCjPdR2pwMfcPKb2zJtIhUsUiXvrCqLUvpsn8jz1/f9auHyYwgmqXFlvrwbfz3nAlML3U6ukF8D78y77IOAdoGnGAjvIFeTONivpje8dM4wxxpg+R9I5dBfbAJgWEdMK1/WFQr4XEbPy54eAz+Wdy66CpgO4S9IpwL3AtYXqdwAzgW9Luo0UhtHB5jnId+VxzwD3ALMi4lekIrfPZ6e4EbsB/1647qR7N/ZDtXLPuaLdroUxD9eN2Y20E97ZoH+j5+TiJKtIZbRXbsa7NWMcm/4S0oj9gI9JmgysAb4eEQtIzvpoUijLDsD/KBQqaYgd5AqyX18bYIwxxpSE7AxPayJpFmIBKbThIrrjZYmIhZL2IZUOOI4U0nBURCzL9xfnHdDTSSEKG5nUs6mxOsf/fgw4FpiVwyIeLwolnUUK8xgKHA0Ni6G0KvDV05hmc23Jc5obkX75GAf8zWbIBwIfIMU6/zWpqt4+pB309aRQkQ8Av5Z0T7OqenaQK0gHe7c13oVCjDHGmERE3JfLKI+s618NzAHmSNoAnAgsK0huBaaSwhuGFvpfJYVSFBlCOuhHjgGeD8yXtAQYC8wG9pQ0JCK6ImIGMEPSUtKhvE5gj8J8uwMv5M8vSRqWd4+HkUpb02RMJxsnjC3OVRvTmcMZdiKVnN4iJB1MOlx3QkS8uhlDOoE5uST1o/l734V0qPHOiHgXeFnSg8DhgB1k080XebavTTDGGGO2JiYDPyY7XJKOAZ7KB8y2BQ5g00NoHcCqiFhSF5t7P3CDpCkR0SXpZGBRRKzPh/02FMI1DgGei4i3JE0nxfyemw/pDaD7AN8CYISkvUkHAseQnEZIjvpYYEr+eUuh/2eSLiPtvI4AHs12dEkaCTwCnAlcXjfXQ8ApwH3ZWX3PSNqT9AvGGRHxzGYO+xfgU6RfHvYjvf9K4HngU5J+SgqxGAn8c7OJ7CBXkH1eaXOC4b1ihjHGGNMfqI9BvjMiJhQFEXGHpOL/XfcFrlI6ebYNcDtwc92YTlLaNer6F0u6Anggp4R7GTg73x4MXC5pZ1Ias+V0x09fCFwCLJXUBbxNOnT3Qo4HHg/MI+0od0TEk3ncFFIowjiSI/n5bMeTkmaTDvKtA76cd68hHYa7hnTgbS7d8cHTgevzgb7XSI44AJJ+DfwFMFhSJzAuIuZJ+grwTVIGjsWS7oiIs4Fvk3bWf5QP8K0rpNS7kbTzvkue6zsRMZ30S0dH3jl/Bxib089dCcwAlpLCQGZExOL6776IttCxN/2Yu5aPaWvRRw2fuUmM0Zp17cUYGWOMMX3B9gMbxs2aiuMd5Aqyz6Q2J7imN6wwxhhjjCkndpCryLF9bYAxxhhjTHmxg1xFftnm+LG9YoUxxhhjTCmxg1xFBvW1AcYYY4wx5cUOcgX5tzYr6TmJhTHGGGO2ZuwgV5B9Vve1BcYYY4wxJSYi3Nw2asA5f+px7/UZ1vcffZlssb7/2GK917bMerfqtW22zK02WznntJa0Pe69PsP6/qMvky3W993c1vetvky2bA16UzHsIBtjjDHGGFPADrIxxhhjjDEF7CCbRkz7Dxj3Xp9hff/Rl8kW6/tubuv7Vl8mW7YGvakYioi+tsEYY4wxxpjS4B1kY4wxxhhjCthBNsYYY4wxpoAdZGOMMcYYYwrYQTbGGGOMMaaAHWRjjDHGGGMK2EE2W4ykD0maLmluvj5A0jjry6cvky3We22rqi+TLda31puK09e1rt36bwPmAqcCi/L1QGCJ9eXTl8kW6722VdWXyRbrW+vdqt28g2zaYZeImA1sAIiIdcB660upL5Mt1nttq6ovky3Wt9abCmMH2bTDm5KGAgEgaSSwyvpS6stki/Ve26rqy2SL9a31psr09Ra2W/9twKHAg6T/wDwIPAMcbH359GWyxXqvbVX1ZbLF+tZ6t2o3l5o2bSFpILA/IODpiHjX+nLqy2SL9V7bqurLZIv1rfWmuthBNm0h6WhgL9JhBwAi4jrry6cvky3W966+TLZY31xfJlusb6031WVga4kxjZF0PbAvsJDugw4B9PQ/Buv7SF8mW6z32lZVXyZbrG+tN9XGO8hmi5G0DDggNvMfkfV9py+TLdb3rr5Mtljff2yx3pjmOIuFaYelwIet7xf6Mtlife/qy2SL9f3HFuuNaYJDLEw77AI8JelRYG2tMyI+a33p9GWyxfre1ZfJFuub68tki/Wt9abC2EE27TDR+n6j/1PObX3f6v+Uc1vfu/o/5dzW977eVJkoQa45t/7bgI8Ax+XPOwBDrC+nvky2WO+1raq+TLZY31rvVt3W5wa49d8GfAlYAPwuX48A7rW+fPoy2WK917aq+jLZYn1rvVu1W58b4NZ/GylVzrbAE4W+JdaXT18mW6z32lZVXyZbrG+td6t2cxYL0w5rI+Kd2oVShaKwvpT6Mtlivde2qvoy2WJ9a72pMHaQTTv8StI/AIMkfRr4OfB/rS+lvky2WO+1raq+TLZY31pvKowLhZgtRtI2wDhgFCBgHnB19PCPyvq+05fJFuu9tlXVl8kW61vrTbWxg2yMMcYYY0wB50E2W4ykJWwav7UKeAyYFBGvWl8OfZlssb539WWyxfrm+jLZYn1rvak2dpBNO8wF1gM/y9dj8s8/ANcAf2d9afRlssV6r21V9WWyxfrWelNlogSpNNz6ZwMe7KmPBqlzrO87fZlssd5rW1V9mWyxvrXerdrNWSxMOwyWdGTtQtIRwOB8uc76UunLZIv1Xtuq6stki/Wt9abK9LWH7tZ/G/DXwBLgWWAFsDj3vR841fry6Mtki/Ve26rqy2SL9a31btVuzmJh2kbSTqSMKG9YX259mWyxvnf1ZbLF+v5ji/XGNMYhFqZtImIV8FPry68vky3W966+TLZY339ssd6YxthBNr3Fbtb3G32ZbLG+d/VlssX6vpvb+t7Xm4phB9n0Fk9Y32/0ZbLF+t7Vl8kW6/tubut7X28qhmOQjTHGGGOMKeBCIWaLkXQMMBH4COnfkoCIiH2sL5e+TLZY77Wtqr5MtljfWm8qTpQglYZb/2zAb4ATgF2BobVmffn0ZbLFeq9tVfVlssX61nq3ajfvIJt2WBURc63vF/oy2WJ97+rLZIv1/ccW641pgmOQzRYjaQowAJgDrK31R8Tj1pdLXyZbrPfaVlVfJlusb6031cYOstliJP2yQXdExKesL5e+TLZY37v6MtlifXN9mWyxvrXeVBs7yMYYY4wxxhRwHmSzxUjaSdJlkh7L7dJcwtP6kunLZIv1Xtuq6stki/Wt9aba2EE27dABdAGn5vYHYIb1pdSXyRbrvbZV1ZfJFutb602ViRKk0nDrnw1YuDl91ve9vky2WO+1raq+TLZY31rvVu3mHWTTDm9L+pvahVIS9retL6W+TLZY77Wtqr5MtljfWm+qTF976G79twGHAIuAFbk9ARxsffn0ZbLFeq9tVfVlssX61nq3ajdnsTBbjKTtgFOAfYGdgVWklDkXW18ufZlssd5rW1V9mWyxvrXeVBtX0jPtcAvwBvA48HvrS60vky3W966+TLZY339ssd6YJngH2WwxkpZGxIHWl19fJlus7119mWyxvv/YYr0xzfEhPdMO/yrpIOv7hb5Mtljfu/oy2WJ9/7HFemOa4B1ks8VIegoYDjxLqmsvUjzXwdaXS18mW6z32lZVXyZbrG+tN9XGDrLZYiR9pFF/RDxnfbn0ZbLF+t7Vl8kW65vry2SL9a31ptrYQTbGGGOMMaaAY5CNMcYYY4wpYAfZGGOMMcaYAnaQjTHGGGOMKWAH2RhjjDHGmAL/H8B7njrgQwFHAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, col_cluster=False, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 467, "metadata": {}, "outputs": [], "source": [ "nw['cpg_val'] = cpg_list_bins" ] }, { "cell_type": "code", "execution_count": 468, "metadata": {}, "outputs": [], "source": [ "nw = nw[nw['cpg_val'] > 0.0001]" ] }, { "cell_type": "code", "execution_count": 490, "metadata": {}, "outputs": [], "source": [ "nw = nw.T" ] }, { "cell_type": "code", "execution_count": 491, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 495, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 462, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = sns.scatterplot(y=nw['cpg_val'], x=nw['con_var'])\n" ] }, { "cell_type": "code", "execution_count": 394, "metadata": {}, "outputs": [], "source": [ "nw = nw[nw['cpg_val'] > 0.020]" ] }, { "cell_type": "code", "execution_count": 402, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'columns' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cpg_val'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'columns' is not defined" ] } ], "source": [ "nw = nw.drop(columns=['cpg_val'])" ] }, { "cell_type": "code", "execution_count": 396, "metadata": {}, "outputs": [], "source": [ "nw = nw.T" ] }, { "cell_type": "code", "execution_count": 360, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1109.4849853515625" ] }, "execution_count": 360, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.max().max()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 290, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "non-gene False\n", "non-gene False\n", "non-gene False\n", "non-gene False\n", "non-gene False\n", " ... \n", "non-gene False\n", "non-gene False\n", "non-gene False\n", "non-gene False\n", "non-gene False\n", "Length: 106242, dtype: bool" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.sum(axis=0) != 0" ] }, { "cell_type": "code", "execution_count": 496, "metadata": {}, "outputs": [], "source": [ "nw = nw.loc[(nw.sum(axis=1) != 0), (nw.sum(axis=0) != 0)]" ] }, { "cell_type": "code", "execution_count": 398, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24, 77)" ] }, "execution_count": 398, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.shape" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [], "source": [ " rank_abs = lambda x: stats.rankdata(x, method='ordinal')\n", " \n", " nw_ranked = np.apply_along_axis(rank_abs, 1, nw.to_numpy().astype('float64'))" ] }, { "cell_type": "code", "execution_count": 229, "metadata": {}, "outputs": [], "source": [ "nw_ranked_high = np.where(nw_ranked > 9000, nw_ranked, 0)" ] }, { "cell_type": "code", "execution_count": 230, "metadata": {}, "outputs": [], "source": [ "nw = pd.DataFrame(nw_ranked_high.T, columns=genes_intersect).T" ] }, { "cell_type": "code", "execution_count": 493, "metadata": {}, "outputs": [], "source": [ "nw = nw.loc[(nw.sum(axis=1) != 0), (nw.sum(axis=0) != 0)]" ] }, { "cell_type": "code", "execution_count": 232, "metadata": {}, "outputs": [], "source": [ "nw = nw.loc[(nw.sum(axis=1) != 0), (nw.var(axis=0) >= 756509182)]" ] }, { "cell_type": "code", "execution_count": 473, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24, 32)" ] }, "execution_count": 473, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.shape" ] }, { "cell_type": "code", "execution_count": 228, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[84670, 90314, 50573, ..., 48087, 84404, 90414],\n", " [67272, 37826, 45204, ..., 81363, 85779, 32191],\n", " [ 0, 0, 0, ..., 43996, 43722, 41450],\n", " ...,\n", " [ 0, 48281, 48485, ..., 0, 0, 0],\n", " [46079, 19959, 64616, ..., 23837, 0, 96010],\n", " [60473, 99155, 85315, ..., 70756, 25619, 25620]])" ] }, "execution_count": 228, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw_ranked_high" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "108436828.10229886" ] }, "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.var(axis=0).min()" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(30, 99666)" ] }, "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.shape" ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2486974128.202299" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(nw_ranked).var(axis=0).max()" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([8.59377752e+08, 1.15965166e+09, 6.64114484e+08, ...,\n", " 8.03111890e+08, 7.58785113e+08, 9.54217844e+08])" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw_ranked.var(axis=0)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ADWZ2M7AL+BiAu28xsw3ASyQz0D7j7qV0vU8D3wBagR+mf5AksG+Z2XaSM5e1aV29ZnYn8Gy63BfdvTdQO0WmlDh2Ht+yl1s3PM9AIaYlF3H3DZeweuUioqhW77FIGJYc8EtnZ6d3dXU1OgyRk7aj5yjX3fNjBgpxpawlF/GDW65m+fwZDYxMTkdmttndO2s9pm/yi5xmug8PDEkuAAOFmH1HBhoUkZyplGBETjMLZ7XQkhv61m7JRSyY2dKgiORMpQQjcppZNq+du2+4pJJkymMwy+a1NzgyOdOEHOQXkQaIImP1ykVcdMvV7DsywIKZLSyb164Bfpl0SjAip6EoMpbPn6FBfWkodZGJiEgQSjAiIhKEEoyIiAShBCMiIkEowYiISBBKMCIiEoQSjIiIBKEEIyIiQSjBiIhIEPomv4xLHDs79/exv2+QpkxEf77Ewln6CRIRGZ0SjIypfAGrux7fyq93nss9T27ThaxETlL5oK378MBpe7CmLjIZ0879fdy64Xk+9O4lleQCyTVGbt3wPDv39zU4QpHppXzQdt09P+Y3/s/TXHfPj3l8y97T7tLWSjAypvIFrMzQhaymsDh2dvQc5alX3mJHz9HT7sPqdFI+aDvdD9bURSZjqr6AVUsuGnEpXl3IqvHKR8TlDy11X05t9a46ejr9ArbOYGRM5QtY/e2/vsEtv7BCF7Kags6UI+LTxZly1VGdwciYKhewWjST3r5BHlp/pWaRTTFnyhHx6aJ80Db8jPN0O1hTgpFxOd0uYFVrBg8wbWf1lI+I1X05PZwpVx1VgpEzTq3xiq/eeCn5ok/bMYxTdUQcYursmTAd90ScbgdttSjByBmn1njFC7sPce8/7RgxhnHRLVdPiw+AekfE4/2ADzFRQJMPzmwa5JczTq3xitin/xTs8hHxlcvPYvn8GZXkMt7vW4SYKKDJB2c2JRhpmEZ9b6PWDJ6McVrO6pnIB3y9iQInKkSdMn0owUhDNPKbzOXxiurp1u9a2jGirHoMo5FfYjyZbU/kAz7E1NkzZTqu1KYxmGluug6gjnZkPRljHqONVwCjjmGEGkcY6/U72W1PZHbZsnntfPXGS3lh9yFiT87q3rW046Smzp4p03GlNiWYaWw6D6A2+nsbtWbwlM8MfNgJQqhkOJ7XbyLbrpWsJvoBny96ZbJDedmTcaZMx52uQh+gKsFMY9UfPos7WvjoZUv52d7DLJndyruWdEzpN/FU+95GvQ/77sMDzGlr4qOXLcXSp/SRzbtPOhmOJ3mMNxHXi3+0s7UdPUeHfLCESqRnwnTc6WgyDlA1BjPF1et/L3/4LO5o4RNXnsf9/28H92zazq/f+9SU/2XWWuMgjew6qTcYvrijhZuuSp7frz65nft+vIObrjqPRbNOLhmOZ3xkwczxjWHUi3/47DKg5viXBuTPLJMxw09nMFNMHDu7evvoPjxIvlSit6/A5x95oeYRRvks4KOXLa35M/pT+TscobpOxnPKX2uZeh+u82e08JVNQ5/fr2zaxqqLF51UrOXkMdpZXBw7r+4/ymevWVHZ/miJeCJdjq++VfuD5aH1V02ps0oJazK6qZVgAjjRfs04dp58uZtt3Ud58Nld3Lb6HXz+kReGdM+8vPcwFy+eybKzZlTOAn6293DwHSVEX+2p7joZzyn/aMu8feHMUT9cR3sj9hwd4IIFJxb7eJLHzv19/NZfPcectiZu/vnlmEFkcPHimSMmAhRLPu7k8Fpv35Bu1XK3X7FU5K5fe/eIA5pz57SN6E4b7YuatfaRqToR5UQPRqZC7Cequj1tTdngBxRKMKdI9SWF3zw4MORN+tUbL+WCs2aw78ggTkw2yvDW0UEWd7SycvEsstmkC2Tn/j62dR9l09a93HLNCvLFEnPamvjN9y3jy3//b5X6zpvXzrlz2ytnAefMaeXR599g7c+dy9I5bfQPFjnQnz/pLpxyu3b19vGTXQf5q6d3ctP7lrOrt5/uwwNcfs4cmpoydZ+PyXxjjmcMYef+Pu56fGvlAxvgrse38sAnrxgyGH7evFbuXPOuyiWiy2/E8odyJoLWXJY49hNq16tv9fHn/7id//SBC/najZdRiB0czpqRZfNrvXQfSbZb/eaf0Zxhyew2/q37CAClGPYdGaCtKcNXNr3MHR9ayRe+v4W3LZjB+g9cQIzTny+yvfsIPUcHK69De1OW8+a1cuMV51X2q/PmtbJsbhsPPvsaf3z9exjIFzlnbhuXLpnNj7Z2j9lPP1riXvWOheNav5Z6+9DJ7l/FYszfvbhn1N6B6jbd9fhWPvTuJWQi+Lnz5nLV8nmV92y937Q7dCxPKabme320di7uaKm8rqf6fTP8NTpvXit/+JF38j/+5sVgM/zMh0+ZOY2Y2WrgK0AGuM/dvzTasp2dnd7V1TXhbQwMFNl18Ch7Dg1y1swmBgvOjfc9zXUrF/KpDywnF8GOnmMc7B9k0exm3jpS5NlXe/jk1cth6EExPUcGOThQZEZTln2Hj3HO3HZe7j7CA0/t5Hd+aQUXLphJ9a4Wpfvqzv3HGCyUAOeCBTOIGLlDRhHE8dhl1eVRBFv39FEoFXno2df51PuX09GWZU5bU2ULterIZmBbdz8lL3HB/JmjLluKIZetH8NE4n3z4ADf/pfX+NyqFbRko5rrdx8a5LXeY7TmjIvO7iBTtX4pbXMmbfeLu/dz9dsWctbMJl576xiZjBPHEcW4WLddY7UBkli7D+d58md7uOUX38b27n7eOnyUBR0zMIu5YP5M8sWYzz70PJ/5dxdwwcJ2jg6UmNOeozUX8ebBQea058hFEW8eHOAnrx/i+y+8wR/8yjs4MhiTLxRZubSDnT3H6BvMV9oK4Ab7Duf5z9/eXNlPC8WY//l3L/Frl5/Lc6+9xU3vX04GGEjLf3f125nVnBu1bccKMf/rB8ly7bkcUQQ2rLze+sPLowhe2ZfsQ29bOHPIslEEe9P32/B9fTz7TRRB9+E86791vP2ZGsuWY//EVctoyRnnzhn6wZvNwGv7B5jZmqm0LUrf7y1NsP9okWd29HDT+5PPgeFxlWJozsGWN/v45+3dfPLq5by6r59Mxkdsa7xtq1c+WIz5w2GvZSmGo/kSPUcGWdzRwrvP7hj1oHE0ZrbZ3TtrPna6JhgzywD/BvwSsBt4FvgNd3+p1vInkmAGBop0vb6fllyGYhxTLDmHB0r8/Za9fPTyJQBkoojX3upjTnuOjrZmvrd5F792+VIKpZHPezaTYaBQwt3JRBH9+SJN2YhCoUR7y9CTzVzG6M/HtDVl2fLmYWa1ZFg6t5VijXpzGRuxvVplw8vNIh745x386mXn0pKLKMVOc9Yqb+nR6nWMQ/152puzNesFKJSc1qbolMbbkstwrFCiqc76zbksew72M6c9SZLl8r7BEi1NGTKWvGZ//ZPXueHnzuVYoUT/YIm25gxN2QwH+wbrtms8bTCLcIdvPrWDT1y1jGxle+ex/+gA7c3ZyrLZKGKwFNPedLxtmSjiWKFUeR2ymYiunQfZse8Iv3HleZX2NWUzdB86VmlrOYbYI/YeHuDHL++r7Kf5klMqwcaX3uTDlyypLNtfiIkwMtHIdlQrxklCid3JZo7vI4XYx7X+8PJsJuJQf57ZbbkRy1a/38b7nFfLZiLeOprnyZe6K+2vtWx/wWnNRhTjErnqBqTLmiWvQ6Yqx+XSfWR2WzOPbN7Fhy9ZUjOG8v5fio1Hn3udD1+yhGwm4li+OGJbE2lbvWUHCklZufq+wRK9/UW+8LdbKmcwd655J796yZKaZ1qjOVMTzFXAH7j7ten92wHc/Y9qLX8iCebZV/dTTGdqRWbENZ7LQ8cKtDZlaMpEHOjLM7u9iUydZduaMuQyEQf7C7Q1Z2jJZmouG5kxWCzRN1iiGDvzZoxeb63YRou3urwcQy4yHCjFPmTnH63egWKJpmFvkuHLFkoxzXXadiLxFkvJh1u99QcKJTKRVdpRLt9/NM+8GU1A8jrMas1VPiTLj5Xrn2hcw8sO9hcoxs6s1ixNmaiyvXwxpjl7PK7BYglgzOc8mzEG8jHZjJEvxWniTJK3M/I1K8+Mqy43g9f293PO3LbjZ2Yn0LZCKa75ATnR5yxfimnKRCcUw1hl+VJc83010WWLcUxkQ/eH8j5ysL9AR1uyD9Wqt7z/9/blK8uNtq2JtG0iy+4/mue/PfyvI8ZgHlp/Je85Z86IekdTL8GczmMwS4DXq+7vBt5bvYCZrQfWA5x77rkT3sDew4OUE7RZ8gW9XCaiGMeVL+t1tOboGyhyKHZmt+V468hgZdnhOlpzHOgrgMHs1hw9RwZH3eHMktPbOW05SrHXrbdW+XiWLcfT2pShUIwpxk51d/Bo9WbMODRsivTwZWOHTFQ4pfHGngyC11vfzChVtaNcfixfqjzPHa3J61RWfiybiSiW4pr1TqQNHa3Ja3agr0Bkx7eXy0QcPlaoLFve1FjPOSTdqwtmNZMxI19K2pfNROSL8Yj125qyGMnYQLmuKDLamrJD2m2WPKfDO1zrta38GpQ541t/eHk2Svahk92na5VlI2MgKpEvxUMeG23Zwz5yP03yijH8AL28j1TvQ7XqLe//s1qOLzfatibStnrLDn8tj+VLNSev7D00wHvOGVnviTidE0ytkbEhT7u73wvcC8kZzEQ3sGhWM8XYKcZOUyY6fgSXzVW2lM0YLU0Z4tg5ViiysKMFnJpJA5yzZjRRcmegGDO7NUdzLkO2xiBf+Qi3GMe0NWdpa85gnNozGHAWdjRzqL9Aay4il43GdQZTKMVkhsU8GWcw5aPneusfOlagozU78gymL8+89uQMpvya5Ysx2cgqj0Xpm3SicY0sc/LF5LnFj29ve/eRymSE8uvbny/R0ZqjGDvZKDk7y5fiEfvEKz1HuXjJLPoGisxoidLnIel3H/6a/ckTW/nd1RexINdS+a5UMXa2vnmI95w7p1JWjmH4Gcloba5+DUa7P97nrBQ7maj22Wj1+238z/lx5boxhnwi1NpHI0vOdmvVW+t1KO8j5fd6HHvdM5jY48py5R6CUGcwB/rzdLQeHwfb35evOYtsUYdmkY3HbqA6Dy8F3jyVG3jX4g66Xt9PseTkMskOUoodK3klu1UOeA0KxRhvSvrOSzW+BFkoeWV/j+OYYvnwr2rcoyzKGKU4Jl+Mib1Ic9ZoyljNeqMa5bXKhpcXSuX+dE9mOZXA7HjbRqu3UIrTw6jRt1eKvdLtdqriLcWOmdd8HsrLGU4xjivtKJfnIsiXkv70Upz0WSdnKzbksREfSifwnBdKzmA6aFG9vZktWY7lCzRlo8rrC06+VEo/9JPXuBxX9T5xwfx2DvcPApaMh6TtK7lj8dDX7IbOc3mkKxkLLMdYLDmLOlroH8wfT75pDNWveb02V78GNsr98T5nhZKDJQlm+LLV77fxPufVynUPn2RTax8tArmsUyqNrLdYihn+zixB5b1eyiWvW60Yyvv/QCGmtckr+0WtbU2kbfWWtXRfKvfy5iK441dWjhiDWbm4Y0SdJ+p0HoPJkgzyXwO8QTLIf6O7b6m1/MnMItt54CjF0vEZUeXZSNW7XrmsUHQyWas5q6S8XDF2ogg8hnwck42gJTtyZkdSX7K8kxyVNWftlM3KKsfjJFMc43QbuaqjttHqLRRHPgehZ5GV6yzPBBtt/XzRIYKmtB3V5ZmsUf1MD3/das2vmegssvI+MlCMyUQ+5LXtz8dks9AURUNe31zW8Kq21Xp+y+sS25D2Dd8fowgO9ZfoKxRZNKu5kjDLMTVlIZdOdxvva1lt+PZqvR9O9rUcbd8Z735TK6Zay5b3/6ZRtlXruSmr3p9GiyGXHVpHiPdEddlAfuh+nC86fYUSB/ryLJndyjvP7pjQAD+coWMw7l40s98CniCZpvwXoyWXk9HSkuWixbNPdbUiItPeaZtgANz9B8APGh2HiMiZSD92KSIiQSjBiIhIEEowIiIShBKMiIgEcdpOU54oM+sBXjuJKs4C3jpF4Uy26Rw7KP5GU/yN1ej4z3P3+bUeUII5Rcysa7S54FPddI4dFH+jKf7Gmsrxq4tMRESCUIIREZEglGBOnXsbHcBJmM6xg+JvNMXfWFM2fo3BiIhIEDqDERGRIJRgREQkCCWYk2Rmq83sZTPbbma3NTqeWszsHDP7BzPbamZbzOyzaflcM9toZtvS/3Oq1rk9bdPLZnZt46KvxJMxs+fM7Pvp/ekU+2wze9jMfpa+BldNs/h/O91vXjSz75pZy1SO38z+wsz2mdmLVWUTjtfMLjezn6aP3WNmtX6Vf7Li/5N0/3nBzP7azGZP1fiHcHf9neAfyWUAXgGWA03AvwIXNzquGnEuBi5Lb88kuU7OxcAfA7el5bcBd6W3L07b0gycn7Yx0+A23Ar8FfD99P50iv0B4D+mt5uA2dMlfpJLj78KtKb3NwC/OZXjBz4AXAa8WFU24XiBZ4CrSC7V8kPg3zcw/lVANr1911SOv/pPZzAn5wpgu7vvcPc88CCwpsExjeDue9z9J+ntI8BWkg+ONSQffqT/P5LeXgM86O6D7v4qsJ2krQ1hZkuBXwbuqyqeLrHPIvnAuB/A3fPufpBpEn8qC7SmF/FrI7ky7JSN393/CegdVjyheM1sMTDL3Z/y5NP6m1XrBFUrfnf/kbsX07v/QnKF3ikZfzUlmJOzBHi96v7utGzKMrNlwKXA08BCd98DSRICFqSLTbV2/Rnwewy9yO10iX050AP8ZdrFd5+ZtTNN4nf3N4A/BXYBe4BD7v4jpkn8VSYa75L09vDyqeBTJGckMMXjV4I5ObX6NKfsvG8zmwE8AnzO3Q/XW7RGWUPaZWYfAva5++bxrlKjrJGvSZaku+Pr7n4p0EfSRTOaKRV/OlaxhqT75Wyg3cw+Xm+VGmVT9j3B6PFOyXaY2e8DReA75aIai02Z+JVgTs5u4Jyq+0tJug+mHDPLkSSX77j799Li7vRUmvT/vrR8KrXr/cCHzWwnSRfkL5jZt5kesUMSz253fzq9/zBJwpku8f8i8Kq797h7Afge8D6mT/xlE413N8e7oarLG8bM1gEfAv5D2u0FUzx+JZiT8yywwszON7MmYC3wWINjGiGdPXI/sNXd76566DFgXXp7HfBoVflaM2s2s/OBFSQDhpPO3W9396Xuvozk+X3S3T/ONIgdwN33Aq+b2dvTomuAl5gm8ZN0jV1pZm3pfnQNyRjedIm/bELxpt1oR8zsyrTdN1WtM+nMbDXweeDD7t5f9dDUjn+yZxWcbn/AdSSzsl4Bfr/R8YwS48+TnB6/ADyf/l0HzAM2AdvS/3Or1vn9tE0v04DZJ6O044Mcn0U2bWIHLgG60uf/b4A50yz+LwA/A14EvkUyY2nKxg98l2S8qEByJH/zicQLdKZtfgX4KukvnzQo/u0kYy3l9++fT9X4q//0UzEiIhKEushERCQIJRgREQlCCUZERIJQghERkSCUYEREJAglGBERCUIJRiQwMzs62XWa2br0p+m3pd8AF5l0+h6MSGBmdtTdZ0xWnWY2l+SLnZ0kX7DdDFzu7gdOZQwiY9EZjMgkMrPfNbNn0wtHfSEtu8vM/kvVMn9gZr8z2vLjcC2w0d1706SyEVh9qtsiMhYlGJFJYmarSH4r6gqSn4+53Mw+QPIjnr9etegNwP+ts/xYpupP5ssZJtvoAETOIKvSv+fS+zOAFe5+v5ktMLOzgfnAAXffZWa31Foe+KcxtjMlfqpdRAlGZPIY8Efu/r9rPPYwcD2wiOSMZqzl69lN8sOgZUuBf5xgHSInTV1kIpPnCeBT6YXfMLMlZla+suKDJJcjuJ4k2Yy1/FjbWWVmc9ILhq1Ky0Qmlc5gRCaJu//IzN4BPJVcooOjwMdJrti5xcxmAm/48Uv7jrr8GNvpNbM7Sa5XBPBFdx9+jXqR4DRNWUREglAXmYiIBKEuMpFpyszeRXKFyWqD7v7eRsQjMpy6yEREJAh1kYmISBBKMCIiEoQSjIiIBKEEIyIiQfx/hlZV7fN8OzUAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(data=nw.var(axis=0).reset_index().reset_index(), x='level_0', y=0)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "y = nw.var(axis=0).reset_index()" ] }, { "cell_type": "code", "execution_count": 418, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, col_cluster=False, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": 497, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", " warnings.warn(msg)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, col_cluster=False, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": 454, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, col_cluster=False, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": 363, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", " warnings.warn(msg)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, col_cluster=False, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, cmap=\"Blues\", vmax=10)" ] }, { "cell_type": "code", "execution_count": 1075, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, cmap=\"Blues\", vmax=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": 1067, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", " warnings.warn(msg)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": 1048, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":2: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " df_dist.values[[np.arange(df_dist.shape[0])]*2] = 0\n" ] }, { "ename": "ValueError", "evalue": "The matrix argument must be square.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdf_dist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mnw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdf_dist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_dist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlinkage_dist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinkage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquareform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_dist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'average'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/scipy/spatial/distance.py\u001b[0m in \u001b[0;36msquareform\u001b[0;34m(X, force, checks)\u001b[0m\n\u001b[1;32m 2215\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2216\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2217\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The matrix argument must be square.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2218\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchecks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2219\u001b[0m \u001b[0mis_valid_dm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: The matrix argument must be square." ] } ], "source": [ "df_dist = nw.max().max()-nw\n", "df_dist.values[[np.arange(df_dist.shape[0])]*2] = 0\n", "linkage_dist = hc.linkage(sp.distance.squareform(df_dist), method='average')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 267, "metadata": {}, "outputs": [], "source": [ "marker_list = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/biccn_class_markers.csv')\n", "marker_list['gene'] = marker_list['gene'].str.upper()\n", "#marker_list = marker_list[marker_list['rank'] < 250] \n" ] }, { "cell_type": "code", "execution_count": 316, "metadata": {}, "outputs": [], "source": [ "df_optimal_marker = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/optimal_number_markers.csv')\n", "\n" ] }, { "cell_type": "code", "execution_count": 317, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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hierarchy_levelmarker_setn_genesf1
1171subclassL2/3 IT10.601276
1172subclassL2/3 IT20.687854
1173subclassL2/3 IT50.786807
1174subclassL2/3 IT100.847541
1175subclassL2/3 IT200.863682
...............
1335subclassVip5000.935080
1336subclassVip10000.904264
1337subclassVip20000.823741
1338subclassVip50000.674320
1339subclassVip100000.637578
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169 rows × 4 columns

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" ], "text/plain": [ " hierarchy_level marker_set n_genes f1\n", "1171 subclass L2/3 IT 1 0.601276\n", "1172 subclass L2/3 IT 2 0.687854\n", "1173 subclass L2/3 IT 5 0.786807\n", "1174 subclass L2/3 IT 10 0.847541\n", "1175 subclass L2/3 IT 20 0.863682\n", "... ... ... ... ...\n", "1335 subclass Vip 500 0.935080\n", "1336 subclass Vip 1000 0.904264\n", "1337 subclass Vip 2000 0.823741\n", "1338 subclass Vip 5000 0.674320\n", "1339 subclass Vip 10000 0.637578\n", "\n", "[169 rows x 4 columns]" ] }, "execution_count": 317, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_optimal_marker[df_optimal_marker['hierarchy_level'] == 'subclass']" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 6)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m6\u001b[0m\n\u001b[0;31m df_optimal_marker['f1'].diff() /\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "df_optimal_marker = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/optimal_number_markers.csv')\n", "\n", "\n", "df_optimal_marker['n_genes'].diff()\n", "\n", "df_optimal_marker['f1'].diff() / \n", "\n", "df_optimal_marker.diff() / df_optimal_marker.index.to_series().diff().dt.total_seconds()\n", "\n", "df_optimal_marker[df_optimal_marker['hierarchy_level'] == 'class']" ] }, { "cell_type": "code", "execution_count": 963, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3169: DtypeWarning: Columns (15,16) have mixed types.Specify dtype option on import or set low_memory=False.\n", " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n" ] } ], "source": [ "marker_list = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/biccn_cluster_markers.csv')\n", "marker_list['gene'] = marker_list['gene'].str.upper()\n", "#marker_list = marker_list[marker_list['rank'] < 250] \n" ] }, { "cell_type": "code", "execution_count": 964, "metadata": {}, "outputs": [], "source": [ "df_optimal_marker = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/optimal_number_markers.csv')\n", "df_optimal_marker = df_optimal_marker[df_optimal_marker['n_genes'] >= 10]\n", "#df_optimal_marker = df_optimal_marker[df_optimal_marker['n_genes'] <= 500]\n", "#df_optimal_marker['f1'] = [0 if x > 0.8 else x for x in df_optimal_marker['f1']]\n", "#df_optimal_marker = df_optimal_marker[df_optimal_marker['f1'] <= 0.8]\n", "#df_optimal_marker = df_optimal_marker[df_optimal_marker['f1'] >= 0.8]\n", "df_optimal_marker = df_optimal_marker.loc[df_optimal_marker.groupby('marker_set')['f1'].idxmax()]\n", "#df_optimal_marker = df_optimal_marker.loc[df_optimal_marker.groupby('marker_set')['f1'].idxmin()]\n" ] }, { "cell_type": "code", "execution_count": 882, "metadata": {}, "outputs": [], "source": [ "marker_list['cell_type'] = np.random.permutation(marker_list['cell_type'].values)" ] }, { "cell_type": "code", "execution_count": 914, "metadata": {}, "outputs": [], "source": [ "marker_list['gene'] = np.random.permutation(marker_list['gene'].values)" ] }, { "cell_type": "code", "execution_count": 915, "metadata": {}, "outputs": [], "source": [ "marker_list = marker_list.drop_duplicates(subset=['cell_type', 'gene'])" ] }, { "cell_type": "code", "execution_count": 885, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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groupcell_typerankgenerecurrenceaurocfold_changefold_change_detectionexpressionprecisionrecallpopulation_sizen_datasetsscSSsnSSscCv2snCv2snCv3MscCv3snCv3Z
0L2/3 ITVip Serpinf1_31EPHA450.8427345.4102022.606655255.8400300.0953040.830882492.8333336NaNFalseTrueTrueTrueTrueTrue
1L2/3 ITSst Crhr2_12CHRNB350.7520627.5576664.62190999.3579990.1521350.582932492.8333336NaNFalseTrueTrueTrueTrueTrue
2L2/3 ITSst Myh8_33PROX150.70230612.4604117.98035261.6763110.2198970.446298492.8333336NaNFalseTrueTrueTrueTrueTrue
3L2/3 ITL2/3 IT_24SNAPC150.7022479.1472106.44266553.9285890.2015460.445979492.8333336NaNFalseTrueTrueTrueTrueTrue
4L2/3 ITSncg Col14a15KCNE450.6962026.2171473.64529659.4896540.1219740.479271492.8333336NaNFalseTrueTrueTrueTrueTrue
5L2/3 ITSst Myh8_16CEP35040.7745565.4484402.865220161.7046250.0982520.684696492.8333336NaNFalseTrueFalseTrueTrueTrue
6L2/3 ITL5 PT_27TUBE140.7504076.1249702.805338153.9858290.0952080.631181492.8333336NaNFalseTrueFalseTrueTrueTrue
7L2/3 ITSncg Calb1_28DLAT40.6819154.6799753.34162870.3270040.1186420.469068492.8333336NaNFalseTrueFalseTrueTrueTrue
8L2/3 ITL5 IT_39TIAM240.6807894.9793104.02607843.8913510.1298460.439023492.8333336NaNFalseTrueFalseTrueTrueTrue
9L2/3 ITSst Myh8_310SNCAIP40.6409975.6429795.34124831.0522040.1657360.336479492.8333336NaNFalseTrueFalseTrueTrueTrue
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" ], "text/plain": [ " group cell_type rank gene recurrence auroc fold_change \\\n", "0 L2/3 IT Vip Serpinf1_3 1 EPHA4 5 0.842734 5.410202 \n", "1 L2/3 IT Sst Crhr2_1 2 CHRNB3 5 0.752062 7.557666 \n", "2 L2/3 IT Sst Myh8_3 3 PROX1 5 0.702306 12.460411 \n", "3 L2/3 IT L2/3 IT_2 4 SNAPC1 5 0.702247 9.147210 \n", "4 L2/3 IT Sncg Col14a1 5 KCNE4 5 0.696202 6.217147 \n", "5 L2/3 IT Sst Myh8_1 6 CEP350 4 0.774556 5.448440 \n", "6 L2/3 IT L5 PT_2 7 TUBE1 4 0.750407 6.124970 \n", "7 L2/3 IT Sncg Calb1_2 8 DLAT 4 0.681915 4.679975 \n", "8 L2/3 IT L5 IT_3 9 TIAM2 4 0.680789 4.979310 \n", "9 L2/3 IT Sst Myh8_3 10 SNCAIP 4 0.640997 5.642979 \n", "\n", " fold_change_detection expression precision recall population_size \\\n", "0 2.606655 255.840030 0.095304 0.830882 492.833333 \n", "1 4.621909 99.357999 0.152135 0.582932 492.833333 \n", "2 7.980352 61.676311 0.219897 0.446298 492.833333 \n", "3 6.442665 53.928589 0.201546 0.445979 492.833333 \n", "4 3.645296 59.489654 0.121974 0.479271 492.833333 \n", "5 2.865220 161.704625 0.098252 0.684696 492.833333 \n", "6 2.805338 153.985829 0.095208 0.631181 492.833333 \n", "7 3.341628 70.327004 0.118642 0.469068 492.833333 \n", "8 4.026078 43.891351 0.129846 0.439023 492.833333 \n", "9 5.341248 31.052204 0.165736 0.336479 492.833333 \n", "\n", " n_datasets scSS snSS scCv2 snCv2 snCv3M scCv3 snCv3Z \n", "0 6 NaN False True True True True True \n", "1 6 NaN False True True True True True \n", "2 6 NaN False True True True True True \n", "3 6 NaN False True True True True True \n", "4 6 NaN False True True True True True \n", "5 6 NaN False True False True True True \n", "6 6 NaN False True False True True True \n", "7 6 NaN False True False True True True \n", "8 6 NaN False True False True True True \n", "9 6 NaN False True False True True True " ] }, "execution_count": 885, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_list.head(10)" ] }, { "cell_type": "code", "execution_count": 965, "metadata": {}, "outputs": [], "source": [ "marker_list_optimal_marker = []\n", "for marker, n_genes in zip(df_optimal_marker['marker_set'].tolist(), df_optimal_marker['n_genes'].tolist()):\n", " #print (n_genes)\n", " #marker_list_optimal_marker.append(marker_list[(marker_list['cell_type'] == marker) & (marker_list['rank'] >= (1000 - n_genes ))])\n", " \n", " marker_list_optimal_marker.append(marker_list[(marker_list['cell_type'] == marker) & (marker_list['rank'] <= 10)])\n", " " ] }, { "cell_type": "code", "execution_count": 966, "metadata": {}, "outputs": [], "source": [ "marker_list = pd.concat(marker_list_optimal_marker)" ] }, { "cell_type": "code", "execution_count": 967, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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groupcell_typerankgenerecurrenceaurocfold_changefold_change_detectionexpressionprecisionrecallpopulation_sizen_datasetsscSSsnSSscCv2snCv2snCv3MscCv3snCv3Z
0L2/3 ITL2/3 IT_116530403H02RIK50.8427345.4102022.606655255.8400300.0953040.830882492.8333336NaNFalseTrueTrueTrueTrueTrue
1L2/3 ITL2/3 IT_12ADAMTS250.7520627.5576664.62190999.3579990.1521350.582932492.8333336NaNFalseTrueTrueTrueTrueTrue
2L2/3 ITL2/3 IT_13COL23A150.70230612.4604117.98035261.6763110.2198970.446298492.8333336NaNFalseTrueTrueTrueTrueTrue
3L2/3 ITL2/3 IT_14MET50.7022479.1472106.44266553.9285890.2015460.445979492.8333336NaNFalseTrueTrueTrueTrueTrue
4L2/3 ITL2/3 IT_15UST50.6962026.2171473.64529659.4896540.1219740.479271492.8333336NaNFalseTrueTrueTrueTrueTrue
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53005SncgVip Sncg6ADRA1B40.8852415.0765602.207089516.4235890.3621750.94048278.0000007FalseFalseTrueTrueTrueFalseTrue
53006SncgVip Sncg7CBLN240.7481584.0946581.896812217.7160610.3273220.70071978.0000007TrueFalseTrueFalseTrueTrueFalse
53007SncgVip Sncg8VWC2L40.7318835.8271253.351171151.9224610.4609590.61296178.0000007FalseFalseTrueFalseTrueTrueTrue
53008SncgVip Sncg9TIAM140.7299494.6580921.98548888.8407560.3397420.63711278.0000007TrueFalseTrueFalseTrueTrueFalse
53009SncgVip Sncg10CDH2040.6727937.6672697.08426149.4760830.6151520.40221978.0000007FalseFalseTrueFalseTrueTrueTrue
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850 rows × 20 columns

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" ], "text/plain": [ " group cell_type rank gene recurrence auroc \\\n", "0 L2/3 IT L2/3 IT_1 1 6530403H02RIK 5 0.842734 \n", "1 L2/3 IT L2/3 IT_1 2 ADAMTS2 5 0.752062 \n", "2 L2/3 IT L2/3 IT_1 3 COL23A1 5 0.702306 \n", "3 L2/3 IT L2/3 IT_1 4 MET 5 0.702247 \n", "4 L2/3 IT L2/3 IT_1 5 UST 5 0.696202 \n", "... ... ... ... ... ... ... \n", "53005 Sncg Vip Sncg 6 ADRA1B 4 0.885241 \n", "53006 Sncg Vip Sncg 7 CBLN2 4 0.748158 \n", "53007 Sncg Vip Sncg 8 VWC2L 4 0.731883 \n", "53008 Sncg Vip Sncg 9 TIAM1 4 0.729949 \n", "53009 Sncg Vip Sncg 10 CDH20 4 0.672793 \n", "\n", " fold_change fold_change_detection expression precision recall \\\n", "0 5.410202 2.606655 255.840030 0.095304 0.830882 \n", "1 7.557666 4.621909 99.357999 0.152135 0.582932 \n", "2 12.460411 7.980352 61.676311 0.219897 0.446298 \n", "3 9.147210 6.442665 53.928589 0.201546 0.445979 \n", "4 6.217147 3.645296 59.489654 0.121974 0.479271 \n", "... ... ... ... ... ... \n", "53005 5.076560 2.207089 516.423589 0.362175 0.940482 \n", "53006 4.094658 1.896812 217.716061 0.327322 0.700719 \n", "53007 5.827125 3.351171 151.922461 0.460959 0.612961 \n", "53008 4.658092 1.985488 88.840756 0.339742 0.637112 \n", "53009 7.667269 7.084261 49.476083 0.615152 0.402219 \n", "\n", " population_size n_datasets scSS snSS scCv2 snCv2 snCv3M scCv3 \\\n", "0 492.833333 6 NaN False True True True True \n", "1 492.833333 6 NaN False True True True True \n", "2 492.833333 6 NaN False True True True True \n", "3 492.833333 6 NaN False True True True True \n", "4 492.833333 6 NaN False True True True True \n", "... ... ... ... ... ... ... ... ... \n", "53005 78.000000 7 False False True True True False \n", "53006 78.000000 7 True False True False True True \n", "53007 78.000000 7 False False True False True True \n", "53008 78.000000 7 True False True False True True \n", "53009 78.000000 7 False False True False True True \n", "\n", " snCv3Z \n", "0 True \n", "1 True \n", "2 True \n", "3 True \n", "4 True \n", "... ... \n", "53005 True \n", "53006 False \n", "53007 True \n", "53008 False \n", "53009 True \n", "\n", "[850 rows x 20 columns]" ] }, "execution_count": 967, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_list" ] }, { "cell_type": "code", "execution_count": 968, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 9, 12, 4, 15])" ] }, "execution_count": 968, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.permutation([1, 4, 9, 12, 15])" ] }, { "cell_type": "code", "execution_count": 969, "metadata": {}, "outputs": [], "source": [ "df_ensg_name = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/genomes_jlee/mouse_geneid_symbol.txt',sep='\\t', names=['gene_id', 'gene'])\n", "df_ensg_name['gene'] = df_ensg_name['gene'].str.upper()\n", "marker_list = marker_list.merge(df_ensg_name, right_on='gene', left_on='gene') \n" ] }, { "cell_type": "code", "execution_count": 970, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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groupcell_typerankgenerecurrenceaurocfold_changefold_change_detectionexpressionprecision...population_sizen_datasetsscSSsnSSscCv2snCv2snCv3MscCv3snCv3Zgene_id
0L2/3 ITL2/3 IT_116530403H02RIK50.8427345.4102022.606655255.8400300.095304...492.8333336NaNFalseTrueTrueTrueTrueTrueENSMUSG00000098097
1L2/3 ITL2/3 IT_12ADAMTS250.7520627.5576664.62190999.3579990.152135...492.8333336NaNFalseTrueTrueTrueTrueTrueENSMUSG00000036545
2L2/3 ITL2/3 IT_13COL23A150.70230612.4604117.98035261.6763110.219897...492.8333336NaNFalseTrueTrueTrueTrueTrueENSMUSG00000063564
3L5 PTL5 PT_44COL23A150.70375816.28199217.25223237.4914650.705847...265.0000007FalseFalseTrueTrueTrueTrueTrueENSMUSG00000063564
4L6 CTL6 CT Gpr1392COL23A160.8768779.2701737.009485128.7485540.048327...79.7142867TrueFalseTrueTrueTrueTrueTrueENSMUSG00000063564
..................................................................
831VipVip Serpinf1_39IGSF1150.90850210.8792726.314092192.6294200.095909...45.0000007TrueFalseTrueFalseTrueTrueTrueENSMUSG00000022790
832VipVip Serpinf1_310FGF1350.9063812.6976881.747461706.8102260.025372...45.0000007TrueFalseTrueFalseTrueTrueTrueENSMUSG00000031137
833SncgVip Sncg1VIP70.97223513.1606302.22655911357.9461580.361444...78.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000019772
834SncgVip Sncg6ADRA1B40.8852415.0765602.207089516.4235890.362175...78.0000007FalseFalseTrueTrueTrueFalseTrueENSMUSG00000050541
835SncgVip Sncg9TIAM140.7299494.6580921.98548888.8407560.339742...78.0000007TrueFalseTrueFalseTrueTrueFalseENSMUSG00000002489
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836 rows × 21 columns

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" ], "text/plain": [ " group cell_type rank gene recurrence auroc \\\n", "0 L2/3 IT L2/3 IT_1 1 6530403H02RIK 5 0.842734 \n", "1 L2/3 IT L2/3 IT_1 2 ADAMTS2 5 0.752062 \n", "2 L2/3 IT L2/3 IT_1 3 COL23A1 5 0.702306 \n", "3 L5 PT L5 PT_4 4 COL23A1 5 0.703758 \n", "4 L6 CT L6 CT Gpr139 2 COL23A1 6 0.876877 \n", ".. ... ... ... ... ... ... \n", "831 Vip Vip Serpinf1_3 9 IGSF11 5 0.908502 \n", "832 Vip Vip Serpinf1_3 10 FGF13 5 0.906381 \n", "833 Sncg Vip Sncg 1 VIP 7 0.972235 \n", "834 Sncg Vip Sncg 6 ADRA1B 4 0.885241 \n", "835 Sncg Vip Sncg 9 TIAM1 4 0.729949 \n", "\n", " fold_change fold_change_detection expression precision ... \\\n", "0 5.410202 2.606655 255.840030 0.095304 ... \n", "1 7.557666 4.621909 99.357999 0.152135 ... \n", "2 12.460411 7.980352 61.676311 0.219897 ... \n", "3 16.281992 17.252232 37.491465 0.705847 ... \n", "4 9.270173 7.009485 128.748554 0.048327 ... \n", ".. ... ... ... ... ... \n", "831 10.879272 6.314092 192.629420 0.095909 ... \n", "832 2.697688 1.747461 706.810226 0.025372 ... \n", "833 13.160630 2.226559 11357.946158 0.361444 ... \n", "834 5.076560 2.207089 516.423589 0.362175 ... \n", "835 4.658092 1.985488 88.840756 0.339742 ... \n", "\n", " population_size n_datasets scSS snSS scCv2 snCv2 snCv3M scCv3 \\\n", "0 492.833333 6 NaN False True True True True \n", "1 492.833333 6 NaN False True True True True \n", "2 492.833333 6 NaN False True True True True \n", "3 265.000000 7 False False True True True True \n", "4 79.714286 7 True False True True True True \n", ".. ... ... ... ... ... ... ... ... \n", "831 45.000000 7 True False True False True True \n", "832 45.000000 7 True False True False True True \n", "833 78.000000 7 True True True True True True \n", "834 78.000000 7 False False True True True False \n", "835 78.000000 7 True False True False True True \n", "\n", " snCv3Z gene_id \n", "0 True ENSMUSG00000098097 \n", "1 True ENSMUSG00000036545 \n", "2 True ENSMUSG00000063564 \n", "3 True ENSMUSG00000063564 \n", "4 True ENSMUSG00000063564 \n", ".. ... ... \n", "831 True ENSMUSG00000022790 \n", "832 True ENSMUSG00000031137 \n", "833 True ENSMUSG00000019772 \n", "834 True ENSMUSG00000050541 \n", "835 False ENSMUSG00000002489 \n", "\n", "[836 rows x 21 columns]" ] }, "execution_count": 970, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_list" ] }, { "cell_type": "code", "execution_count": 971, "metadata": {}, "outputs": [], "source": [ "marker_table = marker_list.pivot(index='gene_id', columns='cell_type', values='rank')" ] }, { "cell_type": "code", "execution_count": 972, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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groupcell_typerankgenerecurrenceaurocfold_changefold_change_detectionexpressionprecision...population_sizen_datasetsscSSsnSSscCv2snCv2snCv3MscCv3snCv3Zgene_id
0L2/3 ITL2/3 IT_116530403H02RIK50.8427345.4102022.606655255.8400300.095304...492.8333336NaNFalseTrueTrueTrueTrueTrueENSMUSG00000098097
1L2/3 ITL2/3 IT_12ADAMTS250.7520627.5576664.62190999.3579990.152135...492.8333336NaNFalseTrueTrueTrueTrueTrueENSMUSG00000036545
2L2/3 ITL2/3 IT_13COL23A150.70230612.4604117.98035261.6763110.219897...492.8333336NaNFalseTrueTrueTrueTrueTrueENSMUSG00000063564
3L5 PTL5 PT_44COL23A150.70375816.28199217.25223237.4914650.705847...265.0000007FalseFalseTrueTrueTrueTrueTrueENSMUSG00000063564
4L6 CTL6 CT Gpr1392COL23A160.8768779.2701737.009485128.7485540.048327...79.7142867TrueFalseTrueTrueTrueTrueTrueENSMUSG00000063564
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831VipVip Serpinf1_39IGSF1150.90850210.8792726.314092192.6294200.095909...45.0000007TrueFalseTrueFalseTrueTrueTrueENSMUSG00000022790
832VipVip Serpinf1_310FGF1350.9063812.6976881.747461706.8102260.025372...45.0000007TrueFalseTrueFalseTrueTrueTrueENSMUSG00000031137
833SncgVip Sncg1VIP70.97223513.1606302.22655911357.9461580.361444...78.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000019772
834SncgVip Sncg6ADRA1B40.8852415.0765602.207089516.4235890.362175...78.0000007FalseFalseTrueTrueTrueFalseTrueENSMUSG00000050541
835SncgVip Sncg9TIAM140.7299494.6580921.98548888.8407560.339742...78.0000007TrueFalseTrueFalseTrueTrueFalseENSMUSG00000002489
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836 rows × 21 columns

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" ], "text/plain": [ " group cell_type rank gene recurrence auroc \\\n", "0 L2/3 IT L2/3 IT_1 1 6530403H02RIK 5 0.842734 \n", "1 L2/3 IT L2/3 IT_1 2 ADAMTS2 5 0.752062 \n", "2 L2/3 IT L2/3 IT_1 3 COL23A1 5 0.702306 \n", "3 L5 PT L5 PT_4 4 COL23A1 5 0.703758 \n", "4 L6 CT L6 CT Gpr139 2 COL23A1 6 0.876877 \n", ".. ... ... ... ... ... ... \n", "831 Vip Vip Serpinf1_3 9 IGSF11 5 0.908502 \n", "832 Vip Vip Serpinf1_3 10 FGF13 5 0.906381 \n", "833 Sncg Vip Sncg 1 VIP 7 0.972235 \n", "834 Sncg Vip Sncg 6 ADRA1B 4 0.885241 \n", "835 Sncg Vip Sncg 9 TIAM1 4 0.729949 \n", "\n", " fold_change fold_change_detection expression precision ... \\\n", "0 5.410202 2.606655 255.840030 0.095304 ... \n", "1 7.557666 4.621909 99.357999 0.152135 ... \n", "2 12.460411 7.980352 61.676311 0.219897 ... \n", "3 16.281992 17.252232 37.491465 0.705847 ... \n", "4 9.270173 7.009485 128.748554 0.048327 ... \n", ".. ... ... ... ... ... \n", "831 10.879272 6.314092 192.629420 0.095909 ... \n", "832 2.697688 1.747461 706.810226 0.025372 ... \n", "833 13.160630 2.226559 11357.946158 0.361444 ... \n", "834 5.076560 2.207089 516.423589 0.362175 ... \n", "835 4.658092 1.985488 88.840756 0.339742 ... \n", "\n", " population_size n_datasets scSS snSS scCv2 snCv2 snCv3M scCv3 \\\n", "0 492.833333 6 NaN False True True True True \n", "1 492.833333 6 NaN False True True True True \n", "2 492.833333 6 NaN False True True True True \n", "3 265.000000 7 False False True True True True \n", "4 79.714286 7 True False True True True True \n", ".. ... ... ... ... ... ... ... ... \n", "831 45.000000 7 True False True False True True \n", "832 45.000000 7 True False True False True True \n", "833 78.000000 7 True True True True True True \n", "834 78.000000 7 False False True True True False \n", "835 78.000000 7 True False True False True True \n", "\n", " snCv3Z gene_id \n", "0 True ENSMUSG00000098097 \n", "1 True ENSMUSG00000036545 \n", "2 True ENSMUSG00000063564 \n", "3 True ENSMUSG00000063564 \n", "4 True ENSMUSG00000063564 \n", ".. ... ... \n", "831 True ENSMUSG00000022790 \n", "832 True ENSMUSG00000031137 \n", "833 True ENSMUSG00000019772 \n", "834 True ENSMUSG00000050541 \n", "835 False ENSMUSG00000002489 \n", "\n", "[836 rows x 21 columns]" ] }, "execution_count": 972, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_list" ] }, { "cell_type": "code", "execution_count": 973, "metadata": {}, "outputs": [], "source": [ "marker_table.fillna(0, inplace=True)" ] }, { "cell_type": "code", "execution_count": 974, "metadata": {}, "outputs": [], "source": [ "marker_table[marker_table != 0] = 1" ] }, { "cell_type": "code", "execution_count": 918, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'df_jac_corr_list' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_2d_jac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgo_chrom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_egad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarker_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_jac_corr_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_2d_jac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'AUC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'DEGREE_NULL_AUC'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'df_jac_corr_list' is not defined" ] } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_table, df_jac_corr_list[2])\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 960, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.619466019740627" ] }, "execution_count": 960, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 762, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6083322739197063" ] }, "execution_count": 762, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 778, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10812, 10812)\n", "(10812, 85)\n", "0.9176111510086832\n", "0.0\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 778, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_exp, go_chrom = run_egad(marker_table, df_exp_corr)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_exp, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_exp['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_exp['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 779, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6383083090649919" ] }, "execution_count": 779, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_exp['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 467, "metadata": {}, "outputs": [], "source": [ "df_exp_hic = df_2d_jac.merge(df_2d_exp, left_on=df_2d_jac.index, right_on=df_2d_exp.index)" ] }, { "cell_type": "code", "execution_count": 480, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'AUC (co-exp)')" ] }, "execution_count": 480, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FL/0u+b09+wOlb4qZwqo78toXZo9gJrDB3bvcfQ+wGDgvx/GXAj8LsT0iNSNbCii5lWOhdXSOnTiSH18xkx9e3sEj187i3BmHZ6zcmSr1Lrm1pTnQgGqxdX50R17bwhwjmAS8lvK4Gzg504FmNhyYC1yT5fV5wDyAKVOmlLeVIlWQLcXy5s6+gu6es/Uspo0tbCC0kAHVYUOMeae3EXNosPhjqW9hBoJMfx3ZVricC/x3trSQuy8CFkF8Y5ryNE+kenKlWAqZa16uQmZB0zcbt/f27wKW2u58u4pJbQszNdQNHJHyeDLwepZjL0FpIYmQcpVSLmchsyDpm1ounFYJtV7Yrlhh9ghWAO1mNg3YQvxif1n6QWY2CvgL4PMhtkWkppRrALXShcxquXBa2AZTGfB0ofUI3H0f8Zz/Y8BaYIm7rzGzq83s6pRDzwd+7e6hFf4YrFFc6luhA6iZ/o4rvUlLvW4KUw61UrMoDIN+8/rBHMWleipd1jjX3zHEL1KVmpqZPPeoTQV95pVtXHrn7wY8v3jeyXy0bWwVWlSYXJvXD/paQ4M5ikt1VGPLwVx/x5WemhnVqaCDuWZR3kBgZh1m9hUzu8XM/tHMPltPq3+jPrgl5VeNmwv9HVffYE6LZR0sNrPLgfnAq8BKYB3QBJwGXG9mq4FvufvmCrSzaFEe3JJwVKOssf6Oq28wr5DONWuoGTjV3d/P9KKZnQi0AzUdCFR5UIIKmvevxkVZf8e1oVb3EyjVoB8shugObklwhUwqqNYEBP0dSylyDRbnDQRm1gbcBpwCxIBniBeH6yp3Q4MoJhCI5NPVs5uzFywPvGK2Hi/KtbCBu1RPrkAQZEHZfxCvInp+4nFyFXDGukEi9ajQvH+9pQg0jVpyCRIIzN3vTXn8UzPLWBxOpB7FYs7wYY3Mn3MUMYen1r3FrKPH09gAQxsaWLFxOy3NB9X1HXS5ahLJ4BQkEDyZ2FRmMfGicRcDjySnkGr/AKlnme6U/+Gc41j41AY2bX+fRU91MX92Oz/v3JxzM/iw2lauVE4tbeAutSfIgrKLgb8DngR+A3wJ+CLxKaVK1ktdy3Sn/J2H13DOjEn9jxcsW885MyZVdCFiuRetDebFUFK6vIHA3afl+GqrRCNFwpLtTtls4ONKLuAq96K1wbwYSkqXNzVkZjcB/8vd9yceHwLc5u5XhN04kbBlWxOQOpku+biSd9DlTuUM5sVQUrogqaEhwO/NbIaZnUW8vPTKcJslUhmZ7pS/+5njefiFLf2P589u5+EXtlT0DjqMVE5UawRJfoEWlJnZJ4CHgLeB0919Q9gNy0brCKTc0tcETBk9nM1vv8fWnX0MH9bI3v0xxlR41pCme0q5lbqg7HTg34CfAicAY4Avunu23cZCpUAgUVGPi9akdpW6oOyfgb9y95cSH3YBsAyYXr4miki6elu0JvUrSCA4JTlQDODu95vZf4XYJhERqaAgg8VHmtkTibLTmNkM4msJRERkEAgSCO4Evg7sBXD3F4jXGxKpC9qzWiS3IKmh4e7+e7MDBqn2hdQeiaiwKmNq9o1IfkF6BNvM7EjidYYws4uAN0JtlURKmHsAa89qkfyCBIIvAz8AppvZFuB/AFeH2SiJljAv1tlW6G7dmb9UhFJKEhV5U0OJDWg+YWbNQIO77wq/WRIlYVbGzFZCYu9+JxbzrOmhWkkpaTMZqYQgPQIA3L2X+IY0g5Lu/qonzMqYrS3N3HzhjANKSMyf3c63fvVizh5HLaSUwkyZiaQKMlicalIoraiyWrn7i6owN2ZvaDAOP7SJK09rwwzc4d5nN/HGu305exy1UL9fm8lIpRQaCP4QSiuqTP/DVVfYlTFbmg/i7qe7BqSHcvU4sqWUKlm/vxaCkURD4NSQmY0E5hfy4WY218zWmdmGxC5nmY45w8xWmdmaaq1YzvU/nFRGqZUxc6X2iqnFXwv1+7WZjFRKkP0ITgB+QrzYHGa2Dfgbd1+d532NxDe9PxPoBlaY2YPJmkWJYw4F/hWY6+6bzWx8sSdSilq4+5PMggyW5kvtFdPjqIX6/WGmzERSBak++lvgm+7+ZOLxGcA/ufvH8rzvFOIb2nwy8fjrAO7+v1OO+XvgcHe/MWiDw6g+qjGC2hT099LVs5uzFywfEMiXZkjt1dssHFUglXIptfpoczIIALj7bxJTSfOZBLyW8rgbODntmKOBoWb2G2Ak8Z3PfpL+QWY2D5gHMGXKlAA/ujC1cPcnAwUduwmaS6/HgK8KpFIJQcYIuszsW2bWmvi6EXg1wPsy/Z+V3v0YAnwY+DTwSeBbZnb0gDe5L3L3DnfvGDduXIAfXTjt3lR7go7dBMmlx2LOi1ve4eU3d3LVrDYmjmrSKmORhCCB4IvAOOD+xNdYIMh+xd3AESmPJwPpm9l0A4+6e6+7bwOeAj4Y4LMlAoIOluYb2E32BC5e9CwLntjAXcu7+MJHp/YHA00KkKgLsrL4bQqcLZSwAmg3s2nAFuIVSy9LO+ZXwO1mNgQYRjx19H+L+FlSg0rNxwcdLM2X2suUYlqwbD1XntbG3U93aVKARF6QWUOPE9+h7J3E49HA4uQgcDbuvs/MrgEeAxqBe9x9jZldnXh9obuvNbNHgReAGHBXvtlIUh9KycfHYs7mHb1s3fknmoY0sOTvTmHPvv059w3OlUvPlmJqbECzcKi/AXQpvyCDxWOTQQDiPYSg0zzdfSmwNO25hWmPbwFuCfJ5Uj+KXaQXiznL1m1l/dbd3PbE+gOCyElTxhR1gco2PXjO9PGcMOnQSF/06nEAXcovyBhBzMz6p+qY2VQGDvqKHKDYRXobt/fyQve7/UEg+b5SBnWzjSFEPQhAbdRUkuoL0iP4JvB0yqrf00lM5RTJpthFelt39hFzylpaQdODs1MZC4EAPQJ3fxQ4Cfg5sAT4sLs/FnbDpL4VW6JhwiFNNBplL62g6cGZqYyFQI6VxWbW6u4bs74xvnflJHfvDqltGYWxsljCUcyq2FxjBMpbl5/GCKIj18riXIHg/xHvMfwKWAn0AE3AUcDHgTnAP7j742E0OhsFgsEvddbQe3v2MWVMM9PGKpUTFpWxiIaiAkHijccCnwNOBSYC7wFric8E+oW7V3wljgKBiEjhiq41lKgU+s1QWiUiIjUh8H4EIiIyOCkQiIhEnAKBiEjEZQ0EZvZJM7sow/OfM7Mzw22WiIhUSq4ewXeATHsIPwH8YzjNEZGw5drfWaIp16yh4e7ek/6ku78ZcIcyEakxWkAmmeTqETQl9gk4gJkNBQ4Or0kiEhYVmZNMcgWC+4E7U+/+E98vTLwmInWm2KqwMrjlCgQ3AluBTWa20syeAzYSLzVxYwXaJiJlpiJzkknWQODu+9z9BuL7Dl8O/A0wxd1vcPe9FWqfiJRRsVVhZXDLOlhsZhekPeXAoWa2yt13hdssEQmD9maQTHLNGjo3w3NjgBlmdqW7LwupTSISolz7O0s0ZQ0E7n5FpucTW1UuAU4Oq1EiIlI5BZeYcPdNwNAQ2iIiIlVQcCAwsw8AfwqhLSIiUgW5BosfIj5AnGoM8Q1qvhBmo0REpHJyDRb/c9pjB7YD6919T3hNEhGRSso1WJyp4BxmdqqZXebuXw6vWSIiUimBxgjM7EQz+z9mthH4LvBywPfNNbN1ZrbBzG7I8PoZZvauma1KfH27oNaL1DFVAZVakWuM4GjgEuBS4imhnxPf7P7jQT7YzBqBO4AzgW5ghZk9mNgHOdVydz+nmMaL1CtVAZVakqtH8DIwBzjX3U9z9+8D+wv47JnABnfvSowpLAbOK76pIoOHqoBKLckVCC4E3gSeNLM7zWwOUMityiTgtZTH3Ynn0p1iZs+b2X+a2XGZPsjM5plZp5l19vQM2CJBpO6oCqjUklxF537p7hcD04HfAF8BJpjZv5nZWQE+O1PQSE+CPgdMdfcPAt8HHsjSlkXu3uHuHePGjQvwo0Vqm6qASi3JO1js7r3u/u+JPP5kYBUwYOA3g27ilUuTJgOvp332Tnffnfh+KTDUzMYGbLtI3VIVUKkludYRDODuO4AfJL7yWQG0m9k0YAvxgefLUg8ws8OAre7uZjaTeGDaXkibROqRqoBKLSkoEBTC3feZ2TXAY0AjcI+7rzGzqxOvLwQuAr5kZvuA94FL3F1z6CQSVAVUaoXV23W3o6PDOzs7q90MEZG6YmYr3b0j02sFF50TEZHBRYFARCTiFAhERCJOgUBEJOIUCEREIk6BQEQk4hQIREQiToFARCTiFAhERCJOgUBEJOIUCEREIk6BQEQk4hQIREQiToFARCTiFAhERCJOgUBEJOIUCEREIk6BQEQk4hQIREQiToFARCTiFAhERCJOgUBEJOIUCEREIk6BQEQk4hQIREQiToFARCTiQg0EZjbXzNaZ2QYzuyHHcR8xs/1mdlGY7RERkYFCCwRm1gjcAXwKOBa41MyOzXLczcBjYbVFRESyC7NHMBPY4O5d7r4HWAycl+G4a4H7gLdCbIuIiGQRZiCYBLyW8rg78Vw/M5sEnA8szPVBZjbPzDrNrLOnp6fsDRURibIwA4FleM7THv8LcL2778/1Qe6+yN073L1j3Lhx5WqfiIgAQ0L87G7giJTHk4HX047pABabGcBY4Gwz2+fuD4TYLhERSRFmIFgBtJvZNGALcAlwWeoB7j4t+b2Z/Qh4WEFARKSyQgsE7r7PzK4hPhuoEbjH3deY2dWJ13OOC4iISGWE2SPA3ZcCS9OeyxgA3P3yMNsiIiKZaWWxiEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxCkQiIhEnAKBiEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxCkQiIhEnAKBiEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxCkQiIhEnAKBiEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxA2pdgNERKImFnM2bu9l684+JhzSRGtLMw0NVrX2KBCIiFRQLOY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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = sns.scatterplot(df_exp_hic['AUC_x'], df_exp_hic['AUC_y'])\n", "ax.set_xlabel('AUC (Hi-C)')\n", "ax.set_ylabel('AUC (co-exp)')" ] }, { "cell_type": "code", "execution_count": 471, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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key_0AUC_xAVG_NODE_DEGREE_xDEGREE_NULL_AUC_xP_Value_xAUC_yAVG_NODE_DEGREE_yDEGREE_NULL_AUC_yP_Value_y
44Pvalb Kank40.7945823265.6044320.2521883.066907e-240.7775373267.2562410.2817713.017425e-21
50Sncg Col14a10.7967823644.1262310.3154235.852151e-040.8014203589.8698450.3609667.914001e-04
67Sst Tac20.8001412950.0688960.1803665.602384e-130.8045972962.4877690.2097902.710819e-13
32Lamp5 Egln3_30.8043033146.0219080.2377481.787500e-480.8110293113.5053140.2426671.764866e-50
14L5/6 NP_10.8086363102.0649170.2261921.719034e-140.7790663051.0006960.2258032.086843e-11
43Pvalb Il1rapl20.8109193158.7428400.2209381.064214e-260.7939013136.9606380.2447011.050272e-23
56Sst Crhr2_10.8118763250.6452880.2291524.004409e-250.7858263091.6099100.2268761.084679e-21
11L5 PT_30.8135673512.8188420.3082251.220838e-260.7968533412.4155530.3214592.423162e-24
16L5/6 NP_30.8138193215.6506210.3105336.691331e-040.6864263292.9621700.2807022.441120e-02
15L5/6 NP_20.8195783342.1817660.1966664.788471e-060.8204012971.5227090.2039761.322160e-05
73Vip Chat_20.8276323255.7374020.2624205.425298e-150.8531162712.1479270.1639933.160676e-17
54Sst C1ql3_20.8276432905.4930570.1720065.552334e-160.7712033014.6049610.2078615.619526e-11
13L5/6 NP CT0.8388223118.7632310.2865233.349440e-070.8286872960.0902420.2250523.487057e-07
53Sst C1ql3_10.8400413051.1970480.1703974.915764e-170.7328373077.7140200.2210731.730012e-08
57Sst Crhr2_20.8404182442.6171240.1728028.919463e-080.7983622623.9280890.1568904.431943e-04
36Lamp5 Pdlim5_20.8439663148.6977670.2263321.433852e-040.3070793004.3733980.2163185.488248e-02
77Vip Igfbp6_20.8488342982.8116310.1851768.743658e-170.7822593139.8745730.2382979.848926e-12
34Lamp5 Pax60.8498223010.6403760.1797572.926481e-080.7849173233.0307230.2939447.424776e-06
7L5 IT_30.8514803086.7036530.1282601.638079e-040.8798382625.4880250.1616712.696118e-05
45Pvalb Reln0.8627082933.2373270.1869992.055571e-080.8283633158.0218640.2315967.060817e-07
\n", "
" ], "text/plain": [ " key_0 AUC_x AVG_NODE_DEGREE_x DEGREE_NULL_AUC_x \\\n", "44 Pvalb Kank4 0.794582 3265.604432 0.252188 \n", "50 Sncg Col14a1 0.796782 3644.126231 0.315423 \n", "67 Sst Tac2 0.800141 2950.068896 0.180366 \n", "32 Lamp5 Egln3_3 0.804303 3146.021908 0.237748 \n", "14 L5/6 NP_1 0.808636 3102.064917 0.226192 \n", "43 Pvalb Il1rapl2 0.810919 3158.742840 0.220938 \n", "56 Sst Crhr2_1 0.811876 3250.645288 0.229152 \n", "11 L5 PT_3 0.813567 3512.818842 0.308225 \n", "16 L5/6 NP_3 0.813819 3215.650621 0.310533 \n", "15 L5/6 NP_2 0.819578 3342.181766 0.196666 \n", "73 Vip Chat_2 0.827632 3255.737402 0.262420 \n", "54 Sst C1ql3_2 0.827643 2905.493057 0.172006 \n", "13 L5/6 NP CT 0.838822 3118.763231 0.286523 \n", "53 Sst C1ql3_1 0.840041 3051.197048 0.170397 \n", "57 Sst Crhr2_2 0.840418 2442.617124 0.172802 \n", "36 Lamp5 Pdlim5_2 0.843966 3148.697767 0.226332 \n", "77 Vip Igfbp6_2 0.848834 2982.811631 0.185176 \n", "34 Lamp5 Pax6 0.849822 3010.640376 0.179757 \n", "7 L5 IT_3 0.851480 3086.703653 0.128260 \n", "45 Pvalb Reln 0.862708 2933.237327 0.186999 \n", "\n", " P_Value_x AUC_y AVG_NODE_DEGREE_y DEGREE_NULL_AUC_y P_Value_y \n", "44 3.066907e-24 0.777537 3267.256241 0.281771 3.017425e-21 \n", "50 5.852151e-04 0.801420 3589.869845 0.360966 7.914001e-04 \n", "67 5.602384e-13 0.804597 2962.487769 0.209790 2.710819e-13 \n", "32 1.787500e-48 0.811029 3113.505314 0.242667 1.764866e-50 \n", "14 1.719034e-14 0.779066 3051.000696 0.225803 2.086843e-11 \n", "43 1.064214e-26 0.793901 3136.960638 0.244701 1.050272e-23 \n", "56 4.004409e-25 0.785826 3091.609910 0.226876 1.084679e-21 \n", "11 1.220838e-26 0.796853 3412.415553 0.321459 2.423162e-24 \n", "16 6.691331e-04 0.686426 3292.962170 0.280702 2.441120e-02 \n", "15 4.788471e-06 0.820401 2971.522709 0.203976 1.322160e-05 \n", "73 5.425298e-15 0.853116 2712.147927 0.163993 3.160676e-17 \n", "54 5.552334e-16 0.771203 3014.604961 0.207861 5.619526e-11 \n", "13 3.349440e-07 0.828687 2960.090242 0.225052 3.487057e-07 \n", "53 4.915764e-17 0.732837 3077.714020 0.221073 1.730012e-08 \n", "57 8.919463e-08 0.798362 2623.928089 0.156890 4.431943e-04 \n", "36 1.433852e-04 0.307079 3004.373398 0.216318 5.488248e-02 \n", "77 8.743658e-17 0.782259 3139.874573 0.238297 9.848926e-12 \n", "34 2.926481e-08 0.784917 3233.030723 0.293944 7.424776e-06 \n", "7 1.638079e-04 0.879838 2625.488025 0.161671 2.696118e-05 \n", "45 2.055571e-08 0.828363 3158.021864 0.231596 7.060817e-07 " ] }, "execution_count": 471, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_exp_hic.sort_values(['AUC_x']).tail(20)" ] }, { "cell_type": "code", "execution_count": 425, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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groupcell_typerankgenerecurrenceaurocfold_changefold_change_detectionexpressionprecision...population_sizen_datasetsscSSsnSSscCv2snCv2snCv3MscCv3snCv3Zgene_id
0allGABAergic1GAD170.941159116.9604729.289078820.4634860.659089...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000070880
1allGABAergic2GAD270.928440139.81141513.987046659.1515660.730005...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000026787
2allGABAergic3ERBB470.92144981.7173835.7364152257.1677530.514809...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000062209
3allGABAergic4KCNIP170.91691932.25203810.796420588.5719930.687830...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000053519
4allGABAergic5RBMS370.90209317.0388693.607831340.7017980.442861...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000039607
5allGABAergic6DLX6OS170.888987140.00220835.287387328.5882680.868152...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000098326
6allGABAergic6DLX6OS170.888987140.00220835.287387328.5882680.868152...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000090063
7allGABAergic7GALNTL670.88695910.8946202.0607491346.1161060.329006...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000096914
8allGABAergic8KCNMB270.884982100.91673527.129502349.6943680.810448...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000037610
9allGABAergic9DNER70.87730010.7495672.845851497.5487670.391116...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000036766
10allGABAergic10KCNC170.8706618.5744771.996219398.3667850.315000...10207.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000058975
11allGlutamatergic1ARPP2170.9750929.0886392.0435541381.6800940.857382...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000032503
12allGlutamatergic2PCSK270.9487715.9753321.728900852.3355330.830496...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000027419
13allGlutamatergic3SV2B70.93062122.5055006.576665385.4671830.949654...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000053025
14allGlutamatergic4R3HDM170.9291134.6721301.2106541241.3083860.774356...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000056211
15allGlutamatergic5BAIAP270.92230110.0451734.054475359.9709400.918787...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000025372
16allGlutamatergic6ANO370.9222249.2731513.662973520.3864380.916820...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000074968
17allGlutamatergic7SLC17A770.92134922.6929544.792771428.3415600.932910...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000070570
18allGlutamatergic8SATB270.90583627.29298911.132994213.2361010.969894...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000038331
19allGlutamatergic9CNKSR270.8932005.5499272.145462435.9769620.860840...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000025658
20allGlutamatergic10RASGRP170.8680118.6710283.882966256.5637820.916874...49843.0000007TrueTrueTrueTrueTrueTrueTrueENSMUSG00000027347
21allNon-Neuronal1QK70.89655217.7437111.6226491620.8109990.086534...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000062078
22allNon-Neuronal2ZBTB2070.87215311.4583961.8381421199.0411960.090719...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000022708
23allNon-Neuronal3APOE70.805082150.2052544.9659242159.3028790.149386...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000002985
24allNon-Neuronal4CST370.78717826.2303971.3020183264.7332580.070708...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000027447
25allNon-Neuronal5SLC1A370.749015306.37371617.0265561109.6402560.334852...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000005360
26allNon-Neuronal6ATP1A270.748797154.1933035.8655151231.3649940.179967...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000007097
27allNon-Neuronal7CSRP170.74273380.9184389.426103381.7430180.271855...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000026421
28allNon-Neuronal8NEAT170.710544101.02741214.488602355.4243190.297360...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000092274
29allNon-Neuronal9DAAM270.70997672.13359316.213876219.3285550.317859...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000040260
30allNon-Neuronal10GATM70.70428954.0128504.299634558.5469160.160198...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000027199
31allNon-Neuronal10GATM70.70428954.0128504.299634558.5469160.160198...8908.8571437TrueTrueTrueTrueTrueTrueTrueENSMUSG00000111138
\n", "

32 rows × 21 columns

\n", "
" ], "text/plain": [ " group cell_type rank gene recurrence auroc fold_change \\\n", "0 all GABAergic 1 GAD1 7 0.941159 116.960472 \n", "1 all GABAergic 2 GAD2 7 0.928440 139.811415 \n", "2 all GABAergic 3 ERBB4 7 0.921449 81.717383 \n", "3 all GABAergic 4 KCNIP1 7 0.916919 32.252038 \n", "4 all GABAergic 5 RBMS3 7 0.902093 17.038869 \n", "5 all GABAergic 6 DLX6OS1 7 0.888987 140.002208 \n", "6 all GABAergic 6 DLX6OS1 7 0.888987 140.002208 \n", "7 all GABAergic 7 GALNTL6 7 0.886959 10.894620 \n", "8 all GABAergic 8 KCNMB2 7 0.884982 100.916735 \n", "9 all GABAergic 9 DNER 7 0.877300 10.749567 \n", "10 all GABAergic 10 KCNC1 7 0.870661 8.574477 \n", "11 all Glutamatergic 1 ARPP21 7 0.975092 9.088639 \n", "12 all Glutamatergic 2 PCSK2 7 0.948771 5.975332 \n", "13 all Glutamatergic 3 SV2B 7 0.930621 22.505500 \n", "14 all Glutamatergic 4 R3HDM1 7 0.929113 4.672130 \n", "15 all Glutamatergic 5 BAIAP2 7 0.922301 10.045173 \n", "16 all Glutamatergic 6 ANO3 7 0.922224 9.273151 \n", "17 all Glutamatergic 7 SLC17A7 7 0.921349 22.692954 \n", "18 all Glutamatergic 8 SATB2 7 0.905836 27.292989 \n", "19 all Glutamatergic 9 CNKSR2 7 0.893200 5.549927 \n", "20 all Glutamatergic 10 RASGRP1 7 0.868011 8.671028 \n", "21 all Non-Neuronal 1 QK 7 0.896552 17.743711 \n", "22 all Non-Neuronal 2 ZBTB20 7 0.872153 11.458396 \n", "23 all Non-Neuronal 3 APOE 7 0.805082 150.205254 \n", "24 all Non-Neuronal 4 CST3 7 0.787178 26.230397 \n", "25 all Non-Neuronal 5 SLC1A3 7 0.749015 306.373716 \n", "26 all Non-Neuronal 6 ATP1A2 7 0.748797 154.193303 \n", "27 all Non-Neuronal 7 CSRP1 7 0.742733 80.918438 \n", "28 all Non-Neuronal 8 NEAT1 7 0.710544 101.027412 \n", "29 all Non-Neuronal 9 DAAM2 7 0.709976 72.133593 \n", "30 all Non-Neuronal 10 GATM 7 0.704289 54.012850 \n", "31 all Non-Neuronal 10 GATM 7 0.704289 54.012850 \n", "\n", " fold_change_detection expression precision ... population_size \\\n", "0 9.289078 820.463486 0.659089 ... 10207.000000 \n", "1 13.987046 659.151566 0.730005 ... 10207.000000 \n", "2 5.736415 2257.167753 0.514809 ... 10207.000000 \n", "3 10.796420 588.571993 0.687830 ... 10207.000000 \n", "4 3.607831 340.701798 0.442861 ... 10207.000000 \n", "5 35.287387 328.588268 0.868152 ... 10207.000000 \n", "6 35.287387 328.588268 0.868152 ... 10207.000000 \n", "7 2.060749 1346.116106 0.329006 ... 10207.000000 \n", "8 27.129502 349.694368 0.810448 ... 10207.000000 \n", "9 2.845851 497.548767 0.391116 ... 10207.000000 \n", "10 1.996219 398.366785 0.315000 ... 10207.000000 \n", "11 2.043554 1381.680094 0.857382 ... 49843.000000 \n", "12 1.728900 852.335533 0.830496 ... 49843.000000 \n", "13 6.576665 385.467183 0.949654 ... 49843.000000 \n", "14 1.210654 1241.308386 0.774356 ... 49843.000000 \n", "15 4.054475 359.970940 0.918787 ... 49843.000000 \n", "16 3.662973 520.386438 0.916820 ... 49843.000000 \n", "17 4.792771 428.341560 0.932910 ... 49843.000000 \n", "18 11.132994 213.236101 0.969894 ... 49843.000000 \n", "19 2.145462 435.976962 0.860840 ... 49843.000000 \n", "20 3.882966 256.563782 0.916874 ... 49843.000000 \n", "21 1.622649 1620.810999 0.086534 ... 8908.857143 \n", "22 1.838142 1199.041196 0.090719 ... 8908.857143 \n", "23 4.965924 2159.302879 0.149386 ... 8908.857143 \n", "24 1.302018 3264.733258 0.070708 ... 8908.857143 \n", "25 17.026556 1109.640256 0.334852 ... 8908.857143 \n", "26 5.865515 1231.364994 0.179967 ... 8908.857143 \n", "27 9.426103 381.743018 0.271855 ... 8908.857143 \n", "28 14.488602 355.424319 0.297360 ... 8908.857143 \n", "29 16.213876 219.328555 0.317859 ... 8908.857143 \n", "30 4.299634 558.546916 0.160198 ... 8908.857143 \n", "31 4.299634 558.546916 0.160198 ... 8908.857143 \n", "\n", " n_datasets scSS snSS scCv2 snCv2 snCv3M scCv3 snCv3Z \\\n", "0 7 True True True True True True True \n", "1 7 True True True True True True True \n", "2 7 True True True True True True True \n", "3 7 True True True True True True True \n", "4 7 True True True True True True True \n", "5 7 True True True True True True True \n", "6 7 True True True True True True True \n", "7 7 True True True True True True True \n", "8 7 True True True True True True True \n", "9 7 True True True True True True True \n", "10 7 True True True True True True True \n", "11 7 True True True True True True True \n", "12 7 True True True True True True True \n", "13 7 True True True True True True True \n", "14 7 True True True True True True True \n", "15 7 True True True True True True True \n", "16 7 True True True True True True True \n", "17 7 True True True True True True True \n", "18 7 True True True True True True True \n", "19 7 True True True True True True True \n", "20 7 True True True True True True True \n", "21 7 True True True True True True True \n", "22 7 True True True True True True True \n", "23 7 True True True True True True True \n", "24 7 True True True True True True True \n", "25 7 True True True True True True True \n", "26 7 True True True True True True True \n", "27 7 True True True True True True True \n", "28 7 True True True True True True True \n", "29 7 True True True True True True True \n", "30 7 True True True True True True True \n", "31 7 True True True True True True True \n", "\n", " gene_id \n", "0 ENSMUSG00000070880 \n", "1 ENSMUSG00000026787 \n", "2 ENSMUSG00000062209 \n", "3 ENSMUSG00000053519 \n", "4 ENSMUSG00000039607 \n", "5 ENSMUSG00000098326 \n", "6 ENSMUSG00000090063 \n", "7 ENSMUSG00000096914 \n", "8 ENSMUSG00000037610 \n", "9 ENSMUSG00000036766 \n", "10 ENSMUSG00000058975 \n", "11 ENSMUSG00000032503 \n", "12 ENSMUSG00000027419 \n", "13 ENSMUSG00000053025 \n", "14 ENSMUSG00000056211 \n", "15 ENSMUSG00000025372 \n", "16 ENSMUSG00000074968 \n", "17 ENSMUSG00000070570 \n", "18 ENSMUSG00000038331 \n", "19 ENSMUSG00000025658 \n", "20 ENSMUSG00000027347 \n", "21 ENSMUSG00000062078 \n", "22 ENSMUSG00000022708 \n", "23 ENSMUSG00000002985 \n", "24 ENSMUSG00000027447 \n", "25 ENSMUSG00000005360 \n", "26 ENSMUSG00000007097 \n", "27 ENSMUSG00000026421 \n", "28 ENSMUSG00000092274 \n", "29 ENSMUSG00000040260 \n", "30 ENSMUSG00000027199 \n", "31 ENSMUSG00000111138 \n", "\n", "[32 rows x 21 columns]" ] }, "execution_count": 425, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_list['gene_id']" ] }, { "cell_type": "code", "execution_count": 443, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(32, 3)" ] }, "execution_count": 443, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_table.shape" ] }, { "cell_type": "code", "execution_count": 446, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 ENSMUSG00000070880\n", "1 ENSMUSG00000026787\n", "2 ENSMUSG00000062209\n", "3 ENSMUSG00000053519\n", "4 ENSMUSG00000039607\n", "5 ENSMUSG00000098326\n", "6 ENSMUSG00000090063\n", "7 ENSMUSG00000096914\n", "8 ENSMUSG00000037610\n", "9 ENSMUSG00000036766\n", "10 ENSMUSG00000058975\n", "11 ENSMUSG00000032503\n", "12 ENSMUSG00000027419\n", "13 ENSMUSG00000053025\n", "14 ENSMUSG00000056211\n", "15 ENSMUSG00000025372\n", "16 ENSMUSG00000074968\n", "17 ENSMUSG00000070570\n", "18 ENSMUSG00000038331\n", "19 ENSMUSG00000025658\n", "20 ENSMUSG00000027347\n", "21 ENSMUSG00000062078\n", "22 ENSMUSG00000022708\n", "23 ENSMUSG00000002985\n", "24 ENSMUSG00000027447\n", "25 ENSMUSG00000005360\n", "26 ENSMUSG00000007097\n", "27 ENSMUSG00000026421\n", "28 ENSMUSG00000092274\n", "29 ENSMUSG00000040260\n", "30 ENSMUSG00000027199\n", "31 ENSMUSG00000111138\n", "Name: gene_id, dtype: object" ] }, "execution_count": 446, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genes_intersect" ] }, { "cell_type": "code", "execution_count": 447, "metadata": {}, "outputs": [], "source": [ "y = marker_table\n", "y = y.sort_values(by=['GABAergic', 'Glutamatergic', 'Non-Neuronal'])\n", "genes_intersect = y.index.intersection(df_jac_corr_list[2].index)\n", "#genes_intersect = marker_list.gene_id\n", "nw = (df_jac_corr_list[2].loc[genes_intersect, genes_intersect])\n", "\n", "marker_table = y.loc[genes_intersect, :]\n", "\n", "species= y.idxmax(axis=1)\n", "\n", "lut = dict(zip(species.unique(), sns.color_palette(\"hls\", 3)))\n", "#lut = dict(zip(species.unique(), \"grrbrrryry\"))\n", "lut = dict(zip(species.unique(), \"rgb\"))\n", "#lut = dict(zip(['Brain-Astrocytes', 'Brain-Endothelial cells', 'Brain-Microglial cells','Brain-GABAergic neurons'], sns.color_palette(\"hls\", 4)))\n", "row_colors = species.map(lut)\n" ] }, { "cell_type": "code", "execution_count": 408, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Non-Neuronal': 'r', 'Glutamatergic': 'g', 'GABAergic': 'b'}" ] }, "execution_count": 408, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lut" ] }, { "cell_type": "code", "execution_count": 422, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "g = sns.clustermap(nw, row_colors=row_colors, row_cluster=False, col_cluster=False, cmap=\"Blues\")" ] }, { "cell_type": "code", "execution_count": 398, "metadata": {}, "outputs": [], "source": [ "import scipy.spatial as sp, scipy.cluster.hierarchy as hc" ] }, { "cell_type": "code", "execution_count": 401, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"ENSMUSG00000005360 0.777829 0.551794 \n", "ENSMUSG00000007097 0.297928 0.962437 \n", "ENSMUSG00000022708 0.799726 0.215603 \n", "ENSMUSG00000025372 0.109728 0.999202 \n", "ENSMUSG00000026421 0.159905 0.996370 \n", "ENSMUSG00000026787 0.819048 0.071991 \n", "ENSMUSG00000027199 0.510306 0.749427 \n", "ENSMUSG00000027347 0.607790 0.580084 \n", "ENSMUSG00000027419 0.771690 0.031636 \n", "ENSMUSG00000027447 0.358026 0.695047 \n", "ENSMUSG00000032503 0.744527 0.035351 \n", "ENSMUSG00000036766 0.830972 0.048480 \n", "ENSMUSG00000037610 0.845849 0.047234 \n", "ENSMUSG00000038331 0.827697 0.042405 \n", "ENSMUSG00000039607 0.780292 0.005879 \n", "ENSMUSG00000040260 0.661932 0.669982 \n", "ENSMUSG00000053025 0.697618 0.474416 \n", "ENSMUSG00000053519 0.828794 0.078040 \n", "ENSMUSG00000056211 0.549849 0.851835 \n", "ENSMUSG00000058975 0.323338 0.980206 \n", "ENSMUSG00000062078 0.680279 0.862681 \n", "ENSMUSG00000062209 0.845031 0.000798 \n", "ENSMUSG00000070570 0.117249 0.996324 \n", "ENSMUSG00000070880 0.446122 0.884768 \n", "ENSMUSG00000074968 0.805795 0.001063 \n", "ENSMUSG00000090063 1.000000 0.096827 \n", "ENSMUSG00000092274 0.096827 1.000000 \n", "ENSMUSG00000096914 0.867359 0.003443 \n", "ENSMUSG00000098326 0.944279 0.426646 \n", "\n", " ENSMUSG00000096914 ENSMUSG00000098326 \n", "ENSMUSG00000002985 0.015433 0.544767 \n", "ENSMUSG00000005360 0.711480 0.564733 \n", "ENSMUSG00000007097 0.067356 0.495283 \n", "ENSMUSG00000022708 0.979487 0.331139 \n", "ENSMUSG00000025372 0.016646 0.331278 \n", "ENSMUSG00000026421 0.020063 0.432424 \n", "ENSMUSG00000026787 0.904503 0.491580 \n", "ENSMUSG00000027199 0.365514 0.514660 \n", "ENSMUSG00000027347 0.710738 0.426703 \n", "ENSMUSG00000027419 0.981171 0.281074 \n", "ENSMUSG00000027447 0.095993 0.494864 \n", "ENSMUSG00000032503 0.942736 0.306028 \n", "ENSMUSG00000036766 0.989296 0.373524 \n", "ENSMUSG00000037610 0.986644 0.382005 \n", "ENSMUSG00000038331 0.971285 0.403082 \n", "ENSMUSG00000039607 0.998275 0.154387 \n", "ENSMUSG00000040260 0.641906 0.472285 \n", "ENSMUSG00000053025 0.856071 0.429778 \n", "ENSMUSG00000053519 0.959912 0.389244 \n", "ENSMUSG00000056211 0.622549 0.417043 \n", "ENSMUSG00000058975 0.041720 0.512539 \n", "ENSMUSG00000062078 0.491327 0.562992 \n", "ENSMUSG00000062209 0.999959 0.163497 \n", "ENSMUSG00000070570 0.010615 0.451661 \n", "ENSMUSG00000070880 0.230914 0.466028 \n", "ENSMUSG00000074968 0.999716 0.175473 \n", "ENSMUSG00000090063 0.867359 0.944279 \n", "ENSMUSG00000092274 0.003443 0.426646 \n", "ENSMUSG00000096914 1.000000 0.244899 \n", "ENSMUSG00000098326 0.244899 0.999988 \n", "\n", "[30 rows x 30 columns]" ] }, "execution_count": 401, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw" ] }, { "cell_type": "code", "execution_count": 410, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":2: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " df_dist.values[[np.arange(df_dist.shape[0])]*2] = 0\n" ] } ], "source": [ "df_dist = nw.max().max()-nw\n", "df_dist.values[[np.arange(df_dist.shape[0])]*2] = 0\n", "linkage_dist = hc.linkage(sp.distance.squareform(df_dist), method='average')" ] }, { "cell_type": "code", "execution_count": 423, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 423, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cell_typeGABAergicGlutamatergicNon-Neuronal
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ENSMUSG000000402600.00.01.0
ENSMUSG000000530250.01.00.0
ENSMUSG000000535191.00.00.0
ENSMUSG000000562110.01.00.0
ENSMUSG000000589751.00.00.0
ENSMUSG000000620780.00.01.0
ENSMUSG000000622091.00.00.0
ENSMUSG000000705700.01.00.0
ENSMUSG000000708801.00.00.0
ENSMUSG000000749680.01.00.0
ENSMUSG000000900631.00.00.0
ENSMUSG000000922740.00.01.0
ENSMUSG000000969141.00.00.0
ENSMUSG000000983261.00.00.0
ENSMUSG000001111380.00.01.0
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
cell_type
L2/3 IT0.5602642991.5830000.5027761.685788e-10
L5 ET0.6905352924.9993240.3833664.085962e-02
L5 IT0.5833722927.5245120.4794714.829361e-18
L5/6 NP0.5405002958.5669100.4597362.019075e-05
L6 CT0.5290783001.8704540.4859901.307929e-03
L6 IT0.7260442503.7301350.3229071.509950e-03
L6 IT Car30.5373322968.6436070.4760886.316638e-05
L6b0.5279022996.3236540.4858082.401511e-03
Lamp50.5785902988.5018370.4965963.844387e-16
Pvalb0.6429732646.9498370.3117846.174595e-02
Sncg0.6225752813.5826850.3863231.417964e-03
Sst0.7872772054.6166910.1783196.544910e-04
Vip0.5810472872.6775410.4313535.161788e-17
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value\n", "cell_type \n", "L2/3 IT 0.560264 2991.583000 0.502776 1.685788e-10\n", "L5 ET 0.690535 2924.999324 0.383366 4.085962e-02\n", "L5 IT 0.583372 2927.524512 0.479471 4.829361e-18\n", "L5/6 NP 0.540500 2958.566910 0.459736 2.019075e-05\n", "L6 CT 0.529078 3001.870454 0.485990 1.307929e-03\n", "L6 IT 0.726044 2503.730135 0.322907 1.509950e-03\n", "L6 IT Car3 0.537332 2968.643607 0.476088 6.316638e-05\n", "L6b 0.527902 2996.323654 0.485808 2.401511e-03\n", "Lamp5 0.578590 2988.501837 0.496596 3.844387e-16\n", "Pvalb 0.642973 2646.949837 0.311784 6.174595e-02\n", "Sncg 0.622575 2813.582685 0.386323 1.417964e-03\n", "Sst 0.787277 2054.616691 0.178319 6.544910e-04\n", "Vip 0.581047 2872.677541 0.431353 5.161788e-17" ] }, "execution_count": 342, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac" ] }, { "cell_type": "code", "execution_count": 280, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
cell_type
GABAergic0.75672517.8964570.4306220.003177
Glutamatergic0.68783118.7175240.5820110.052311
Non-Neuronal0.82083318.0331590.4950000.000998
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value\n", "cell_type \n", "GABAergic 0.756725 17.896457 0.430622 0.003177\n", "Glutamatergic 0.687831 18.717524 0.582011 0.052311\n", "Non-Neuronal 0.820833 18.033159 0.495000 0.000998" ] }, "execution_count": 280, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac" ] }, { "cell_type": "code", "execution_count": 808, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8417328042328043" ] }, "execution_count": 808, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 1190, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4618, 4618)\n", "(4618, 80)\n", "0.9378545907319186\n", "0.0\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 1190, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_table, df_jac_corr)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 1191, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.661282315479487" ] }, "execution_count": 1191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 538, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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hierarchy_levelmarker_setn_genesf1
8classGABAergic5000.997599
20classGlutamatergic2000.999537
1177subclassL2/3 IT1000.908418
44joint_clusterL2/3 IT_1500.517432
59joint_clusterL2/3 IT_22000.548169
...............
1110joint_clusterVip Mybpc1_3200.594133
1123joint_clusterVip Serpinf1_1200.414391
1138joint_clusterVip Serpinf1_21000.870805
1150joint_clusterVip Serpinf1_3500.566260
1165joint_clusterVip Sncg2000.837217
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101 rows × 4 columns

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" ], "text/plain": [ " hierarchy_level marker_set n_genes f1\n", "8 class GABAergic 500 0.997599\n", "20 class Glutamatergic 200 0.999537\n", "1177 subclass L2/3 IT 100 0.908418\n", "44 joint_cluster L2/3 IT_1 50 0.517432\n", "59 joint_cluster L2/3 IT_2 200 0.548169\n", "... ... ... ... ...\n", "1110 joint_cluster Vip Mybpc1_3 20 0.594133\n", "1123 joint_cluster Vip Serpinf1_1 20 0.414391\n", "1138 joint_cluster Vip Serpinf1_2 100 0.870805\n", "1150 joint_cluster Vip Serpinf1_3 50 0.566260\n", "1165 joint_cluster Vip Sncg 200 0.837217\n", "\n", "[101 rows x 4 columns]" ] }, "execution_count": 538, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_optimal_marker" ] }, { "cell_type": "code", "execution_count": 961, "metadata": {}, "outputs": [], "source": [ "df_2d_jac = df_2d_jac.merge(df_optimal_marker, left_on=df_2d_jac.index, right_on='marker_set')" ] }, { "cell_type": "code", "execution_count": 346, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Valuehierarchy_levelmarker_setn_genesf1
00.5602642991.5830000.5027761.685788e-10subclassL2/3 IT10000.726512
10.6905352924.9993240.3833664.085962e-02subclassL5 ET100.752232
20.5833722927.5245120.4794714.829361e-18subclassL5 IT20000.753933
30.5405002958.5669100.4597362.019075e-05subclassL5/6 NP100000.742529
40.5290783001.8704540.4859901.307929e-03subclassL6 CT50000.565069
50.7260442503.7301350.3229071.509950e-03subclassL6 IT200.799019
60.5373322968.6436070.4760886.316638e-05subclassL6 IT Car310000.736726
70.5279022996.3236540.4858082.401511e-03subclassL6b10000.772683
80.5785902988.5018370.4965963.844387e-16subclassLamp5100000.645717
90.6429732646.9498370.3117846.174595e-02subclassPvalb100.000000
100.6225752813.5826850.3863231.417964e-03subclassSncg500.765389
110.7872772054.6166910.1783196.544910e-04subclassSst100.000000
120.5810472872.6775410.4313535.161788e-17subclassVip50000.674320
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" ], "text/plain": [ " AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value hierarchy_level \\\n", "0 0.560264 2991.583000 0.502776 1.685788e-10 subclass \n", "1 0.690535 2924.999324 0.383366 4.085962e-02 subclass \n", "2 0.583372 2927.524512 0.479471 4.829361e-18 subclass \n", "3 0.540500 2958.566910 0.459736 2.019075e-05 subclass \n", "4 0.529078 3001.870454 0.485990 1.307929e-03 subclass \n", "5 0.726044 2503.730135 0.322907 1.509950e-03 subclass \n", "6 0.537332 2968.643607 0.476088 6.316638e-05 subclass \n", "7 0.527902 2996.323654 0.485808 2.401511e-03 subclass \n", "8 0.578590 2988.501837 0.496596 3.844387e-16 subclass \n", "9 0.642973 2646.949837 0.311784 6.174595e-02 subclass \n", "10 0.622575 2813.582685 0.386323 1.417964e-03 subclass \n", "11 0.787277 2054.616691 0.178319 6.544910e-04 subclass \n", "12 0.581047 2872.677541 0.431353 5.161788e-17 subclass \n", "\n", " marker_set n_genes f1 \n", "0 L2/3 IT 1000 0.726512 \n", "1 L5 ET 10 0.752232 \n", "2 L5 IT 2000 0.753933 \n", "3 L5/6 NP 10000 0.742529 \n", "4 L6 CT 5000 0.565069 \n", "5 L6 IT 20 0.799019 \n", "6 L6 IT Car3 1000 0.736726 \n", "7 L6b 1000 0.772683 \n", "8 Lamp5 10000 0.645717 \n", "9 Pvalb 10 0.000000 \n", "10 Sncg 50 0.765389 \n", "11 Sst 10 0.000000 \n", "12 Vip 5000 0.674320 " ] }, "execution_count": 346, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac" ] }, { "cell_type": "code", "execution_count": 962, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 962, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(y=df_2d_jac['AUC'], x=df_2d_jac['f1'])" ] }, { "cell_type": "code", "execution_count": 505, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 505, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qbDwZJZEKh4jcICLPichBEbm9xD7XicgTIrJfRH7k2/6iOyL2CRHZ59veLyL3isgL7s/VUZ6DxdIopuYydKUS3h1w3dJxZ3PpuNXWUJg1xGKFMY5aL1R7d6zhkSPnmA950c24qbS9HcmaXVXpTC6rCvJTk4XqK8cNjUyBLYZZd3syznwmSzrTGCGLTDhEJA58BngLsAt4n4jsCuzTB3wWeKeqXga8N/A2b1DVK1R1t2/b7cD3VHUn8D33ucWy5JmeT9OZylkc9Wo5MjHnXCTnM9mq00fNEiQvHddkVdV20Xz1jgFmFjI8cXw01P7pjBKPxVzhqC2rKpPNZVVBzuIQpKbuuIbWsTjchIUGrSdKi2MPcFBVD6vqPPAV4MbAPu8H7lTVYwCqOhzifW8EvuQ+/hLwrvos12JpLpNzaTrbEpG5qqD6OMfhM1NAfoxjMa4qgGsuHCAm4ftWmYt+X2eydleV5rKqgLwOudB8V9XY9AI3f/GnHBmZquo4f4wDGtevKkrh2AQc9z0fcrf5uRhYLSI/FJFHReSDvtcU+K67/Rbf9vWqegrA/bmu2IeLyC0isk9E9p05c2bRJ2OxRM30fIbutriXwVRPV1XCfdNqhGN2IcPbP/VjIJBV5V2kqo+ZAPR2JHn5pt7QcY50Nks87grHItJxE3nCkf8+tWdV1edC/cPnh/nR82f41pMnazp+se7DaolSOIrZf8HfTgJ4FfA24M3AH4nIxe5r16rqK3FcXR8WkddV8+Gq+jlV3a2qu9euXVvl0i2WxjM1l6YzlfBcJz95YYRX/pd7FzWHApy7azO9r5r3euDQSNGsqsWk4xpevWOAx4+dD1VEOJfO0haP0duRqj2rynV3mRiS56oyWVVFdOPOx4a4+s/+pWwco14Wx0OHzwHw8JFzVR1nBM/8ThqVWRWlcAwBW3zPNwNBOR0C7lHVKVUdAe4DLgdQ1ZPuz2Hg6ziuL4DTIrIBwP0Zxr1lsbQ80/MZulJx4u7t/dMnxzg3NV/TDAs/k3MLbF7tzNOoRjj+5UDun5Y/DhCPCW2J2KIuUnt3rGEho+x78XzFfafm0nS1JejrTFYV4H9xZIo3/MUPOTk64xYAOkH+7rZEfnC8RIjjJwdHOD0+x5NDpWtO6hUcf9gdq/vo0fM1Bbg7F+k+rJYoheMRYKeIbBeRFHATcFdgn28CrxWRhIh0AlcDB0SkS0R6AESkC3gT8LR7zF3Aze7jm933sFiWPE46bsJzC52fci5uZ2vsaguw4AbEN6/uBMILh6ryfb9wBF5fbMHZVYOrScYllLtqaj5DZ1uc3o4k85ls6IvjD54b5sjIFEfPTrt1HM7lblV7soirqpBnT00A8MiLpa2AaiyOkcm5okIzPD7L4ZEpLt/cy/R8hv0nx0O/p9HQjhrbwNRKZMKhqmngNuA7wAHgq6q6X0RuFZFb3X0OAPcATwI/Bb6gqk8D64GfiMjP3O3fUtV73Lf+GHC9iLwAXO8+t1iWPJNzabp8WVXz7p3n2cm5mt/TuGQ29VVncew/Oc5L47Pe81hAOWqZyZF3fCrBlVtWVwyQL2SyzKezdKcS9HU4GVFh3VWPHxsFHBHMqnpxnp72hNcxGNzK8YAVs5DJel18Hz1a2iqqRjh+8bMP8Ml7ny/Y/pDrnvp3P78TgJ9W6a6CXOPJRqUHR9odV1XvBu4ObLsj8PzjwMcD2w7juqyKvOdZ4I31XanF0lwybv+krrZEgevk7GTtFsdkBeH4s7sPMNCV4kOv35G3/V8OnM7r4RQLKEc9urG+escAn/r+C4xNL9Dr9qIKYtqwdLquKnCEY2NfR9H9/Tx+3LngZxXSmaznAuxpT+RlURVzVb04MsV8Jsuq9gSPHj1P1u2uGxSYairHR6fn2VdEhB46fJbutgTXXbKW7Wu6ePjIOX7jdReGek9vHocXHF/66bgWiyUkxsXQlUrkBaIBRqZqtziMS2ZVR5Ke9kTBTI4fPDvMTw4W3vV/78AwV27p854Hax3qUal87UVryCo8fKS0u2rKzGFvi9PbEb5f1cjkHMfPOV1sFfWyqiDXfRjc4DiFrqoDLzluqvfu3sLYzAKHzjjWRzBFutpeVQdOjXtjZw0PHT7LVYOrScRj7Bns55EXzxXsU4nFpkhXixUOi6UFMG4ff8sRw6IsjrnccKhildczC5kC98bp8VmeOjHGGy9d720LuqqciXPF/emJeLiCuiu29NGejJWNcxiLo6vNWT8QqgjwCddNBa7F4faqArx+VX6C8fZnT42TiAm/fJWT3/OIG8QPdvWtxlWlOL/no75kh+GJWQ6fmeKaC50eXldt72dsZoEXwg670lx3XKg9RbparHBYLC2Ad4FMJQou0vWIcZQUjvlMQazie25Q/BfyhKOIq6qExZGMh7uspBIxrhrsLxvn8H8vfldVJYybCpx6mIxPOPwWBzjWVPD+/tmXJrhoXTc713Uz0JVi39Fz3nv5qaWO4xlf8PthNw3XCMfV2/sB+GkZK6wYpprfWhwWywrCzOLoTMUL3EK1zgqHXPvwnvZkSYsjeLH5/rOn2by6g4vXd3vbgjZEvbqxXnvRGp4/PcmZieLiaL6XLn+MI4Sr6vFjoyTjudYtmUBw3CBI0YKzZ0+N87ILehARdg+u9gLkBcJRQx2Hv6W8iW9ctnEVAJtXd7Chtz10PYfXqyrlXMptjMNiWUFMzefP4vBbHSMhXVVPnxjja48O5W2b9M34CAqHqjrCEbAcDp+Z4vItfXkCVmBxLDKryrDXbbP+4OHid9iTnqsqTkcyTioeqxjjyGSVnx0f5bKNve5zx6Nj0nGDFgfk9wV7cWSKk2OzvGJzHwC7t/Vz9Ow0wxOzBTGO+Uw2fDzC3e2ZUz6L48g5drvxDXCsnz3b+/npkXNV9SqLi5BKxJhesK4qi2XFYOIFnZ5wOBfqzlScs1NzoS4ib//UT/i9f/5Z3rZyrqq5dBbVQvfG+OyCF08wBDOP6jU46LKNvaxqT/BAkQA95CcNiAi9IfpVHRyeZGo+w6u2OY2zF9y0ZhN7ybM4BJD84Pi3njoFwA0vvwCAVw067/Poi+eLtoGZr7Jgz7iqzkzMcXB40nNTGfZs72d4Yo6jZysXfvqX05mKM7sMWo5YLJaQeC4ZN8hp0l93rO1mdiFb8939xGyaZNyp9A4Kh7nw+99bVRmfSXtzKwxBi6Nerqp4TLjmwoGSAXJ/cBwI1SH38WOOWykoHOYcegLB8aCr6u6nTnHFlj4vhfnlG3tpS8TY56blBqkmsyoZF4Yn5jgzMecVFpq4hiEX5whfzyEiztxxG+OwWFYOhRaHs/2idU6codbMqonZBXrak4gIqzqSzKezXhaVucjMp7NettBcOuvUL3TkX1xjgSuFuUjVo/X73h0DHDs3XbS1yuScScd11tPXUdniePzYKH2dSQYHugC8uR8JL6uqMDhuTI6jZ6fYf3Kct71ig/d6KhHj8s19PH7sfNGOxbMhazkU2LmuB3DSckfcpIct/Z15++1Y201/VypUnMP//Tu/ExvjsFhWDJNBi0OMxeFc/Gqt5ZiYTXt32Ll0VufC67c0jIh4dR8VLI6OlFNEV+18j2LsvWgNAA8WsTqm59PEBNrdcbphWqs/fvw8V2zp87Ko0u7VPl4sOO6elmkWaNxUb3nFBXnvubanjfHZdFFXVTUWx6UbnCD4M6fGPUELZqGJCFcNri7b6iSI4Axzsum4FksLkskq9zz9UtUFWpWYNhXSqfwYx2Itjsm5tHe3HhQOf/2GcVuNz+RiIuUo1VSvlplIO9d1s6a7rWhartOGJdcxuLcjVTY4Pj7r1EBcuWW1Z7WlC2IcAYuDXKzg7qdOcfmWPq+3l6EtEWMunSnuqqqiery3I8mmvg72nxz3YiNticLL8J7tjhV2amym7Pv5V9PIueNWOCyWKvjR88Pc+veP8lCVefbF+PA/PsZf//AQ4FRIp+IxUgmTXePskxOOWi2OhVAWx6zPbQWFF7NiWVXOe+Tf4Ra7CFZCRNi7w4lzBF1f03MZL75hzqGccDx5fAxVuHJrLitsPlPa4nA+3/l57Ow0T58Y520BawOgLRljbiFb1FVVLiX3xOiMl06tqojAZRtX8czJMe+7ThWpe6k2ziFSv4SFMFjhsFiq4PnTTkWvmYy3GB4+fNZLzXQ648a912IiJOPi3flW0yHXf1c8MZumu80RDE84XFeP/+7UiMh9L5ihZ/lCUawAEAoHB7Ul4tTC3h0DDE/McSjwvU4Gvpe+ziSTc+mS88qfcAv/Lt/S51kcJjged89hlS9jzFRxqPrcVC/PxTcMbYk4c+lscVdVGYvjt/7hMf7s7gN523ZtXMXhkSnGZhZIxKSgDxg4Lq3utkRF4fAvp71OKdJhsMJhsVTBIbcVxNGzixMOVWVsZsG7EE3NZehK5e6EYwJ9nSnak3F62hJeIDUM6YBwrCphccwEYhz/+8mTfOzbzxZ9z4LuuCVcVbVYHODM54DCcbJTPlcb4DU3PH6+eKrq48dG2bG2i96OpGdxLLgiYywOpwNx7hhBUJRvP32Kyzf3FgSrzXnNpTMFLUegfIxjfGaB0el80d+1YRWqTt1NqSr7eMwpPAxtcSBOOq51VVksrYdpdndkZHHDlWYWMixk1LMOnOl/+RaHaSM+0J2qKsaRyROO0q6qGV+x2IOHzvJ7X83VgARjFcFqdmNxBO9wUzUKx9aBTjav7uCBg/kuwOm5TN73YqrZXzhd2MtJVXn8+ChXbnXScD2Lw/0+TIxDRDwx8iYA4twUvNJN4Q3iCEdxi6NcVtVCJuu5yhTHjrt4vcmsmij7fV012M8Lw5NlOwf4V2PTcS2WFkRVPVfKYi0Oc/E2F/mp+XSeL19EWN3pdIMd6G7jbBVZVels1lvv5Fyablc4VhVYHLk75f96z7OsW9WW+/zAexZrcui8R30sDnDcVQ8ePuuJqapyYnSGga7cunasNcIxUXD8sXPTnJua58qtfe6a3awq46ry5RQHA+SqkFEtaQG0JeOoFo9nlLM4Mln1LB6DaZ0yOZcuKxwmnbhUO5Y8xGRVWeGwWFqKc1PzjM0s0JmKc/Tc9KIyq8zF27zF9HyGrrwYB96Miv6u2iyO6fkMWc1dJOMxoact4bM48i8yf3PzVd7joIVRrK26+Qw/7cnaYhzguKvGZha8uM/+k+OcGJ3h9Rev9fbpakuweXVH0e6xZnDTlVtWu2t2tnuV4z71CwbIFchmC2M5BiOIxS7M5YLj6ax6n+8/B0OxwLj3mvuZpeI5EKjjCGRVjc8u8J39Ly2qSWYpIhUOEblBRJ4TkYMicnuJfa4TkSdEZL+I/MjdtkVEfiAiB9ztv+3b/49F5IR7zBMi8tYoz8FiMRhr4zUXrWE+nc2bkFctJkCdi3GkvYsxwJruNgYHOt3HqdD9qiAX4/D3qTKs6kh6MzmCOf/GhQKVLY56xzgg17fKxDm+u/8lYgJvvHRd3n4713XzfBGL44njo3Sm4p47y4jAgusq8ouCqVMR3O64rsVR6jpuzqtYDKFccDyTVS/tVtURs2Q85olYOYvDE45MZStCxJkCuJDJCdXzL03wof//UZ6uYhRtWCITDhGJA58B3gLsAt4nIrsC+/QBnwXeqaqXAe91X0oDv6eqlwLXAB8OHPtJVb3C/S9vwqDFEhUmvmEuZC+O1O6uylkcOeugy+fL//JvXMPvvekSAAa62jg3NRfawjEWR64zbk44+jqTnHeDteX84cEb7+CdeHuy+PyHWrOqANataueidd1e+5Hv7D/NVYP9DHS35e138foeDo9MeS4ow+PHzvNzm3t9DQOd7fMVLA6zNZNVL/MqiLmITxcVjtIWgRl9G8RkUpW1ONzXwnbgNXEnI26nxpwbmw297aGOr4YoLY49wEFVPayq88BXgBsD+7wfuFNVjwGo6rD785SqPuY+nsCZWb4pwrVaLBU5fGaStkTMywB6MUQTulIUxDjm0l67EXDcVObiPNCdIqvh2olDzuIYdxsc+qvA1/a0edbLzHxunGqQwuB4/vPOEsHxtuTiLil7dwzw0yPneOH0BM+dnvAaDfrZub6H+XSWY74WJbMLGfafHOeKLbngdkGMI17GVeUKeLHUWMgJomki+GvXDvK//91rgBAxjiJNEM3HJBOlKybDuKoMpnIccu40Uzx4wRITjk3Acd/zIQov/hcDq0XkhyLyqIh8MPgmIjIIXAk87Nt8m4g8KSJfFJGiaRAicouI7BORfWfOnCm2i8VSFYfOTLF9TReb+jpIJWKLCpAHLY6p+fy0Uz/mjjusrzrjumbMvPFu30VyXU8bwxPOnejMQprVJWZ9B6dUlCoArKerCpw4x/R8hv96z3MAvOmyIsLhFkU+78us2n9yjHRWvcA45MQu7X4f+RZH0ttHxNeWpFKMwz3fay4c4JILHNdeuRTYtN9VhXqxIvM55SwO85nlLI5gd1z/Gk+NzdKVctK5602UwlHsNxC0tRPAq4C3AW8G/khELvbeQKQb+BrwEVU1jrq/BnYAVwCngE8U+3BV/Zyq7lbV3WvXri22i8VSFYfOTLJjXTexmLC1v5MXFyEcJs6QzTp3pbML2by0Uz9rupzsqrBxjowaV1Vh+xBjcWSzysx8flV2HhVcVbGY03G3XgWAhmsu7EcE/uXAaV6xqdfrUOvHVNMfHM7FOXKB8b6CNc9n8us4oLBflbH8SlocriVlLKyYiFO8JxWC45ksC+lCF6NZW7kYh0mfDs6JL4bpjgs54XhpbJYLetsLEhvqQZTCMQRs8T3fDJwsss89qjqlqiPAfcDlACKSxBGNf1DVO80BqnpaVTOqmgU+j+MSs1giZS6d4fi5aS8ddHCgM9S8hFJ4rirVvJkTxfAsjpApuRk3HXdyLjf9z7Cup51MVjk3Pc/0fMa72AQJXmqKXXs6U4WVyou1OPo6U940vDdftr7oPl1tCTb1deRZHI8fG2VTXwfrVuXcMmbJuayqYum4zgxAIxylXHeeq8q9KMdjzsW6PRkvGRzPZpWsUtxV5X5OuTG7JquuXIsV9d2Lt6eCrqpZNvQWCm89iFI4HgF2ish2EUkBNwF3Bfb5JvBaEUmISCdwNXBAHIn8G+CAqv6l/wAR8fcDeDfwdGRnYLG4HD07TVZz3WoHB7p48exUzSm5Jv6Qzap38fW31vAz0O1YHGFTctPZfIvD7wJb2+OI0JmJOWYWMgXps6VuTosLR6LAVfXWnyts11Et17oxpDcXcVMZLl7fnZeS+8Tx0Tw3FRRmVfmv0cG28Zmwrir3d2Xu4k1hYDGM5WdiFKo5MTP6VE5oe9oSxGMSasa6UFhbYyyOKKi/88tFVdMichvwHSAOfFFV94vIre7rd6jqARG5B3gSyAJfUNWnReQ1wAeAp0TkCfct/9DNoPpzEbkCx+31IvChqM7B0no8evQcE7NprrtkXeWd64hpNWIsjm1ruphdyDI8MVfTP05/jMMbj1rC4ljdmUKkcowjJk5diPHpjxcRjnU+4ZhdKLQ44iKkVQvcG8XqG9qTOVdVTODDb7iIN9Th9/Ibr7uQl2/qZacvPTjIxet7uP/QWTJZZWRyjhOjM/zatYNF17xQpABw+0AXybi4rUlyRZOVguMmqyrmCUe8ZHDciFGxCYHxEOm4Is76RssMrioV40hnsgxPzLJxqQkHgHuhvzuw7Y7A848DHw9s+wnFYySo6gfqvEzLEuIv732eMxNzjRcONxV3+xpjcTg1Fi+enVqUcGTUaasBlIw3xGNCf2eqYqPDeEzIZtS7YE3OpulKxfPcL8biGJ6YY3o+wwWrAnM3XPUprOMo/OfYmUp4brasFhYJ1sqa7jbecfnGsvtctK7by6x67iUn1mFajRjEvSYXC47vvWgNj/7R9axqTyL4YhwlTsHEOIxQGsukLRkr2XLECFaeq8p9f/N9lnNVQbjBVeB2x/XFOIYn5sgqXLAEXVUWS90ZOj/jzYxoJIfOTLGxt927uJt2ELVmVhnhUFWmvBhH6cBymH5VXvqpr44j2FrDCMeDh84yOr3g+cUN5oJYWMdR+HmmUtlLZa1/DLYkpljx+dMTPH78PMm4eLERQ6HFkb9Af5qycSuVjnHkFwD6XU0jk3O8+7P3c/hMfjW7ESPHCszmZQZ5wfEKwtHbWb6NvP8925O5FOkoazjACodlCZHJKidHZ7wpdY3k8JlJLnTdVOD8g0zGpeZmh/46juDY2GIMdFXuV2UuRp7FMZcuqFfoTCXYsbaLrz02xInRmYJJf+bCGUzHLWZNdLrzH0yYp1S7jijIZVZN8vixUXZt7C2M17g/i7Ucyd9RPKukdMuR/Iwl8bmqnnvJWcPPhkbzjvF3KTZxFkMYVxVUYXEgeQWAL7nCseRiHBZLvRmemHVbKjg+3ESFu7V6YZob/qtX5sqQEvEYW/o7F21xZLLKVGBsbDEGulM8U6F1RG5UqnOhnJhN59VwGO75yOs4MjLF86cneNW21Xz5p8e818y1tVIBIDiukRPzzbE4TGbVM6fGeWpojF++akvBPrl03PKptn5XVSWLw3NVxXLBcVOhb36PhrRPLEycwwiy+T4ruqo6UxwMWDJ+isY45jOcSjvFf1FZHFY4LEuGE+dzYzQnZtOsdusbomZ4Yo7JuTQ71nXnbXcyq6q3OGYXMnmZNlNzlS2ONd1tFWdymGuev+VIb2fhd5SMx7h4fY/n7vn8B3dzctT5bnMWR/C9Cy+oHW46rrmxjqJeoBwXr+/mB88OM7OQKcioctbj/ExXsDhEfK6qUhaHiXEEXFVOOq7z/ub3aDACDq7V47vI+4WnHL1VxDja4jmraHI2TXsy5tWC1BvrqrIsGYYCwtEoghlVhm0DjsURHHdaCb/POqPKlHsX210iqwpgoCvF+GzpyXeQu6P2Yhxz6VBVw9fvWs/NewcB3x13iBiHGRxkqt8brBvsXN/jpTJf6Ws1YgjGfEpZE1C5ANDEIoql4xqCwuGfixL8vYUpAASnt9jEbLqgL5fBX8fhL8o8NT7Lxt6OyMTcCodlyXBiNCccjYxzHHKbGV7o1nAYBge6mJ7PhJuX4MMIh5PApEy7F5yOsq4qJ6hdbqiPuRhlfXUcwRhHJcx7VGo5Ao6rano+47lLGhnjgFzrkYGuFFv6C7OHvCaH6cICwLz9yLmVSnmOEvEY8Zh4FodnMfh6c00GXVV5MY5s3ppMkkKl4LgZ5jUe8kap001YiLKGA6xwWJYQQ75xoQ0VjuFJOlNxLliV/w9x0E3NrdZdZYRjdWeKbNaxOFLxWNm7T1MEWM5dFbzDnqxBOIw7J1SMwy0A9GogmmBxAFy5ta/onXWlrCqDiPjScUufhL/FSi6rKif204FOwf4Yx0Imm2cdXHpB6RoVP32uqzE4ftYQNHY73GFOp0ZnrHBYLOC4qoxroJEpuYfOTLJjbXfBxclfy1ENZhZHX2fSazlSqmrcMODGc8rVcpibV9ORdWYhQ3dbdT7uUq6a4nUc+ZlGzbA4OlNxrrlwoOjr3ujYCsIBldNxwRWOggJAv8VROsYRrC5/2QYndfhwhdb8pu1Ipc7I5qtvT8WZnEtzemIussA4WOGwLCFOnJ/xOpI20uI4fGbKazXiZ1NfB4mYVJ1ZZSyO/q4U2axzwSlVNW4I0yE37rM4Jos0OAxDqeB4qawqyGUTNTo43tWW4Pu/dx3/2o3PBDHrybmhQmRVlbU44mWFo1yMw6Tjmne/atCJyWzsK39xN66qUhZHkI5knOPnp8lkNbLiP7DCYVkiZLPK0OgMu9w7tUYFx2fmM5wYncmr4TAk4jE2r+7gxSprOfJcVapMz+WPjS1GpX5V2axy0s3dz2Szuel/1QqHVwAYIsaRMsKRdvep6qPqwgW97WXTsmMCC9nKWVUm+FzK4gInnuF1x3U/0l87EkzHXQi6qnxupYvW9fDt334t//6NO0t+HvhdVRUsDleSOlNxjriTKjesis7isOm4liXByNQc8+lszuIIOdRosRweKZ5RZdjmNjusBiMcfZ1Jp45jPn9sbDF62hKk4jFGShQBPnlizHuczqhnka2qNjheIsZRKqsK/MLRBOWogIj4mhyWsjjESykub3HEvEB7vJjFMR8+qwrg0g2rCrYFyVkcxf/egxl97cm4l6VnYxyWFY9Jxd020El3W6JhFoeZM75jXaGrCpzeVUfPTleVkjs2s0BPW4JEPEZW3bGxFSwOESnbduS7+1/yHmfyXFXVxTg8i6PI5wfxXFXzzbM4KhGT3AW8ZOU4uXhE+RhH7nfkpePmWRylYxzzgayqsKzqqC7G4W9aubHILJN6UVI4ROTNIvKeItt/RUSuj2xFFksRTPHfpr5OetoTDYtxHBqeRCTXmyrItoFOJufSFRsQ+hmfWWBVR5K4CFlVZ2xsBYsDTL+q4hbHvc+c9tJT01kt2lI9DKUtjtKuqskmxTjC4F9TSVGQynUckG9dFGuLHkzHzYtxpLMFU+zCEI8Jq9oTjIXNqnJ/J6lErOR0x3pQzuL4E+BHRbZ/D/jTaJZjsRTHWBybVnewqj3ptXmImsMjU2xe3VHQB8lQS7PDsZkFejuSXh3H1Hy6bLsRg9OvqvACcmRkiheGJ3mLO587k1UmvCFO1QbHzaNgjKNwXyN20y3sqjLrjklpYQsdHPfVbBSr/C6wOIq0HKmFvs5USYvDfIJZtXEfboho8p+hnHB0qmrBsG5VfQkofvtlsUTE0PlpVncm6W5LOBZHg9Jxj56dKmltgGNxAFU1OxybWaCvM0ks5tQPOMHxsBZHoXDc+4zjpnqzKxz+rKqqg+Nu1LegjqPIlAPjFplsYnC8EmbdpYr/DF6jxjK7+V1V/nkchpmFTJ6VUbQAsPi0iLL0dYZrOwK5YH2w5qjelPs220Wk4K/OHekanfPMYinCidEZNq12/uxWdSS9O+qoOTc1z1o3FbYYm1d3Eq8yJddYHHERtwAwHUo4TL+qYDzlu/tPc9nGVWxzBS6bVa/SONj9thJx97oWnGxYanQs5M/hbjWMmJWLXfiXXSk4HjzGb4VAfhFgxt+rKq1Vt6YxOMOcSgXHzXqcBRkxj7KGA8oLx53A50XEu91yH9/hvlYREblBRJ4TkYMicnuJfa4TkSdEZL+I/KjSsSLSLyL3isgL7s/CJjWWZcfQ+Rk29zl39420OMamF7wirGKkEjE29XVUVT3uuapiwkJWmV3Iehfhcgx0pZhLZ72sGXAm+T167Dxv2nWBF/xNZ52pgsm4VD0D3FxgMwHhKOb799Jx3YtlC+qGJ2blAuN+K6BSAWBwP7/FAfkpuQt1dFWVinEEMX9HUdZwQHnh+M/AaeCoiDwqIo/hjGo9475WFhGJA58B3gLsAt4nIrsC+/QBnwXeqaqXAe8NceztwPdUdSdOvKWoIFmWD6rKifM+i6M92ZDg+EImy8Rcmr6O8l14TbPDsORiHOKlaVYqAITiRYDff/Y0qk6jwtxFP8vE7ALdbYmq/dzeewTujpduOq7zs1zQ27/s8sHxIq6qgMXhrx73Z/6Z33MtX1FfOYuD/MJC46qqVFi4WEoKh6qmVfV2YAvwr4Gbga2qeruqhvlXuwc4qKqHVXUe+ApwY2Cf9wN3quox9zOHQxx7I/Al9/GXgHeFWItlCXNuap6ZhQybXeHoaXfScWs1/cPiFep1lXf3DA50cWQkXJfc2YUMc+ksq9zguKFSyxHw96vK3X1+d/9pNq/u4NINPXmV406Dw+qzakpaHMVmjieCleNVf1zkGCEoZ3H4CRsc91xVieKuqs/+8CB/+PWnvO0LmdqyqsCJcYzNLBS4D/146bipJsc4ROQXReQXce76dwIXAbtFJFx3LtgEHPc9H3K3+bkYWC0iP3Stmg+GOHa9qp4CcH82dvi0peF4GVV9uRiHMzmv+KznemECkpVmGmwb6GRiNs35EAFMU7jY25HMc4uESZtd05VvcUzNpfnxwRGu37UeESEWE69uYXI2XXUqLhROETQUu57GYkJ7MuYLjreecpgVhY5xlNnP38k2HgiOm/eYnEvzt/cf4c/veS7v2IVFuKp6O5KoFu+WELxXybmqohWOcn9Z7yiyrR/4ORH5dVX9foX3LvYbCEpmAngV8EacgPuDIvJQyGPLf7jILcAtAFu3bq3mUEuLYdqpb17txDhMwHdiNlxQuVbGZpw7+74iw5D8bPe65E7RX2G41JhPOPzt2MPWcUCu0eGPXzjDfDrLm3Zd4O2TiMV8Fkf13425cGYDV6RS2UCdqYR3l92CulF1jKNsd9xkrGA/Y3H0dSQ5P73A3z90lLufeonNqzvy5sd4rqoazsFrOzIzXzLeZlySP3/Jev7Dmy/hso29NXxSeEr+ZanqrxXbLiLbgK8CV1d47yEcN5dhM3CyyD4jqjoFTInIfcDlFY49LSIbVPWUiGwAhimCqn4O+BzA7t27o/VpWCLFtFPf5HNVgdPoMMo7q/NTbmuQihZHrpbjlVvL52r4hcN/kQpTx2FEyczk+O7+0/R1Jr2GeeBc+J06jrRnoVVD3LM48reXuu52JONe4VtLWhzumuLxcGsLWzluzrXdFZPVXSnOTy9w91Mv8bqL1/LBa7bxb/9un7f/fEYLrIOw+NuObAs0Ag6+ZW9nkg+/4aLaPqgKqm45oqpHgTDO00eAnSKyXURSwE3AXYF9vgm8VkQSItKJI0YHKhx7F068BffnN6s9B8vS4sT5GXraE57LyLRhiLoIcNTXjLAcW/o7EAlXyzGW56rKbS83NtbQnozT05ZgZNLp2/X954b5+Zety2vyl4gJ6YwyMbtQk8URqyLGAY5PvZWD40YHytVx5LuqSr9XXuV4zGxzxGR9j3MDs2ewn//xq68qCJovxlXVF7K1eiOp+i9LRF4GVBx5pqppEbkN+A4QB76oqvtF5Fb39TtU9YCI3AM8CWSBL6jq0+7nFBzrvvXHgK+KyK8Dx3AzsSzLl6HzM56bCnwWR8QpuaaVdbl0XHAuHht7O0JlVvmFQ6q0OCBXBPiNx08wOr3Au67IDxs6RYXZ2l1VUtxVVUoUOlNxRlyXW0sWAPoqx8NQaZBTcD8jEBt62/mft76aXRtX0ZGK572PvzliLf48TziKpeRGnCBSipJ/WSLyvyi0hPqBDcCvhnlzVb0buDuw7Y7A848DHw9zrLv9LE5MxLJCODGaLxwmxhF1Su7o9AIxIdTc7u1rukLVcoyVCI6HjdUMdLcxPDHLHT86xGUbV/HanWvyXk/ExKvjWEyMoyA4XuJOvD0Z94LjrdirKhfjKGdxhKzj8LWdCdZxtCVj7B7s971n7riOVHyRwXHH4h0rYXE042sv95f1F4HnCpzDEY9fBR6MalEWi0FVGTo/kzflbZUX44jY4piZp68zVTa337BtoJNvPXWq4n7mH79pcmgIU8cBTpzj+88Ok8kqn3n/Kwsu1vGYMDmXJpPVqqf/Qc66SgZiAuUsjqnAONVWwqy7nCAU278YRSvH3W3BQkD/+3Qk44sqAOwt01q9WcHbcsFxfxX3FTg1F78EHAG+FvnKLBacC+3kXNqr4YBcjCPqmRyj0wsVA+OGwYEuRqcXGJ2eL5uFZVqqx2NScFcahjXdKTJZZfuaLm54+QUFrydi4l1garE4/vCtl7JldWdephaUzgbqTMVDzetuFmZJiTLBcf8rVRcAusIRnBfv/y7ak/FFZVWlEjG6UvGS/aqa8a2Xc1VdjBOUfh9wFvgnQFT1DQ1am8XipTT6haMtESMZl8hncozNlG834mebN398misqCIcRPnMXnIrHCi48pRhwazk+9LoLi95Fx+Pi+cJrEY7utgS/ed2Ogu2lRMHfNbhCH8GmEMbiqKVXlX9S4i++chN7d+SnO/k/LhWPFR3kVA1Oh9zCGEeTQhxlXVXPAj8G3qGqBwFE5HcasiqLxSUnHLkYh4g0pO3I+enyDQ79DK7JpeResaWv5H7jbrsRyF3UwlSNG15/yVoODk/y7lcGa2kdErGYV4hYi3CUotT11N9jqxVjHGZJ5QTBT9nuuEUqxwH+8peuKPK5uR2SCcl1x63xK+rtSDJWyuJowvde7i/rX+FYHD9wM5++QnOsIssKxhT/BWsSVnUkI7c4RqcXuHhduEYJW/s7EaHi/PExv3C4t6Vh4xsAVw32c5UvCBskHhOvsryWliOlKB3jSFTcp5mEsjh8j8tbHLkq8UoXa//HJeOxRcU4wG2tXsQ1q02KcpTrVfV1Vf1l4GXAD4HfAdaLyF+LyJsatD7LCmfo/DRdqbiXkmjoaU/w3EvjedXX9aZSZ1w/7ck4G1a1V0zJ9QuHuUhVGhtbDYmYeEkDtbQcKUW5AsBK+zSTUDGOsFlVrqsqjPXiF9H6uKqSxdNxac7dfEWvpKpOqeo/qOrbcSq4n8B2pLU0iCG3K27wDu8D12zjxZFpfuEvf8RXHzle94aHYTvj+tk20MWRKoTDXKPCtBsJi//CV09XVbkCwEr7NJOcxVEmHde/f9l03Fjee4b5XHCC2/MZ08W2tu+otyNVNB23WTGOqsJZqnpOVf+Hqv58VAuyWPycCBT/Gd67ewt3//ZruGR9D//xa0/yvs8/xOEzk3X73LCdcf0MrunkaIVaDn/A3XNV1dniMPTUkI5bilAxjrp9Wv3IVY6HbDlSRhRMk8Mw+igBV9VCXSyOhaI3SM3Q6xbMg7BYcgydn87LqPJz0boevnLLNfy/v/gK9p8c54b//mM+9b0XFu0WgPCdcf0MDnRxbmq+ZKHWXDrD7EK2MDheR4vDf8dc7djYcpTy6ftdVS0ZHHflrJyVELY7rikADFMTUuCqWmyMoyNJOqtezcyhM5M8c3K8aXUcVjgsLcv47ALjs+Wb9cViwvv2bOV7v/t6rt+1nk/c+zxv+6sf8+jRc4v67LCdcf2YZofHSlgd/uI/yPVFqmcsIuEF3OOhi94WQ76rKvKPqxoJZXHkXgtTABjKVeW7siZ9LUdq1dZg25Hf/erPvHkftbq/FoMVDkvLcqJIKm4p1q1q5zPvfyV/c/NupubSvOeOB/nP33iq5pRd0xl3dcjgODiuKqBknMM/iwP8Fkf9XFVGLOppbZTDv/YwFfaNxotx1KU7bnhXlV9cknFZVMsR8LVWn17g3NQ8Tw6NMj670JJ1HBZLUzHCsamEq6oYb7x0PddcOMAnvvs8f/vAEe595jR/8s6XF62yLodJfawmOL613xGOoyPFhWOshHDUc6aI6clUz1TccnQk/em4DfnIqjB3/mXncfheKncOJh03nKvKf1wdsqrcv5mxmQUOj0yhCrNmkJmNcVgsOcwcjlIxjlJ0tSX46Dt28fXfupb+rjZu/ftHueXv9nFqbKbywS5hO+P66UwlWL+qrWSzw6BwmAtQJBZHhAOu/HS0eAGgaYkfpo4jVqE+Ixl32sSEcVXlFQD60nFr/Yb8Fsd9z58BYHoh03p1HBZLsxk6P0N7MsZAhal6pbh8Sx933XYt/+ktL+O+F85w/V/ex989+GJB59diVNMZ18/gQFfJWo5Ci8PZXk0BYCXMnXU9U3HL0dni6bhbXCuwXLaUV11ewZIQEdoSsarTcetVAAhON4Mfv+AKh2txtGQdh8XSLE6MzrCpr7CGoxqS8Rgfev0OvvuR13Pl1j4++s39vOeOB3j2pfGyx1XTGdfP4EDp9upj09G7qszFb1XDXFWtHRw37sPphcrz6cMIQlsiHuo86105bv5mHnnxHKfH59jU18F8Oks2xE1QFFjhsLQswQFOi2HrQCd/92/28N9++QqOnp3m7X/1Ez7+nWeZLXFBqaYzrp9tazoZmZwrOp1wzB08ZdrCm4t8Xes44s1zVbWixbHNFY5To6XdlCYrKUzsohaLI5WIeUHsWr+i9mSc9mSMe585DcD1u9YDjtVh6zgsFh8nRmeqCoxXQkR415Wb+JfffT03XrGJz/zgEDf8t/t44OBIwb7VdMb1M+jNHy+0OsZmFuhuS3ijXqOo44h7wfHGu6paUDc8V9WJcsJRRSPEtmQslMD43yoVMqOrEn0dKabnM1y0rpsda52/s5n5ypZUFEQqHCJyg4g8JyIHRaSgTYmIXCciYyLyhPvfR93tl/i2PSEi4yLyEfe1PxaRE77X3hrlOViaw9RcmnNT81UHxsPQ35XiE790Of/wb69Ggfd/4WF+/59/xvmpXC+g89PzFWeNF6OScPgLCmO+mot6Ya5RjUrHbS8yo6KV2Oq2uz89XrmnWRi3ZFsiXkM6bn0usybO8bqda+lwbzam5zNNqeOI7K9LROLAZ4DrgSHgERG5S1WfCez6Y7cPloeqPgdc4XufE8DXfbt8UlWDEwoty4hSXXHrybUXreE7H3kdf/W9F/jcfYf5/rPDfPTtu7jxio1Vdcb1k5vLURgg98/iAHjFpl5uuOwCLtvUW/tJBIg3OB03FhM6knFmFjItKRyr2pNs6uvgQ6+/sOQ+YYPjED61Nuiqyn1W7d+Ruel43cVrvMB4mNhNFER5W7IHOKiqhwFE5CvAjUBQOCrxRuCQqh6t8/osLUw1xX+LoT0Z5z/e8DLecflG/tOdT/GRf3qCrz02xNnJ+ZpcVV1tCdb2tPFikVoOZxZH7p9cf1eKOz7wqkWtP0ijs6rAiXM4wtGwj6yK+28v31ovTFsSQ1sixnSVdRz1tDhSiRhXbx/gocNnAZieSy+7GMcm4Ljv+ZC7LcirReRnIvJtEbmsyOs3AV8ObLtNRJ4UkS+KyOpiHy4it4jIPhHZd+bMmZpOwNI8aq3hqJVLN6zia7+5lz9552U8dvQ8MwuZqor//AwOFG92GHRVRYGpkK42jXgxmMyqVqzjCIVncVTeNayryv9dpOokHL9y9Tb+6G2X0pGKe0kJ08swxlHs6w3mjj0GbFPVy4FPAd/IewORFPBO4J99m/8a2IHjyjoFfKLYh6vq51R1t6ruXrt2bS3rtzSRodEZUvFY6Al89SAeE27eO8i9v/t6/u1rtvOOyzfU9D7bBrpKuqqiFo6cxdEYVxXkAuRLVTcMYYLjqdBZVfnH1IPXXbyWD7x6EMh95zMLmWVXxzEEbPE93wyc9O+gquOqOuk+vhtIisga3y5vAR5T1dO+Y06rakZVs8DncVxilmWGmcPRjP5HG/s6+M9v38WFa7trOn77mi6GJ+aYns+fUNgQi6PBvaogl5LbijGOMHiV4yH+1rra4qEsiCiC436MlRf8G2sUUf51PQLsFJHtOMHtm4D3+3cQkQuA06qqIrIHR8jO+nZ5HwE3lYhsUNVT7tN3A09HtH5LEzlxfibSwHiUeAHykWl2bVwFwHw6y8xCpoEWR+NdVa0a46iEcSuFCY7/zi9c7E1YLEewyWG98buqWm3m+KJQ1bSI3AZ8B4gDX1TV/SJyq/v6HcB7gN8UkTQwA9yk7qQSEenEycj6UOCt/1xErsBxe71Y5HXLMmDo/Ay/cOm6Zi+jJnIpuVOecATbjURFo+s4IOc2WaoWhyGMq2rn+nCZduIzMvKzqqpeVlFM7c/MfIb2ZP3SucMS6V+X6366O7DtDt/jTwOfLnHsNDBQZPsH6rxMS4sxu5BhZHJuyVocpujs+PlcgDw4iyMqPIujjtP/KtGxxGMcZtn1XH9wkFO9MVZeOqvLLsZhsdSEqeHY3L80haO3I0lvR5Jj5wqFI2qL4xWbe7n2ogHak437p21aqy9Vi6OaOo6w5KXj1ik47qc9GWuqUNt5HJaWY8jM4eiLtoYjSrb256fkBoc4RcWbL7uAN19W3eyRxbLUXVW5tur1FI7iwfF6VXmLOIWX0/MZO4/DYgF/8d/StDjAaXVxvAkWRzPIZVU1eSGLpJ4WR36vqmgusx1NiG0YrHBYWo6h89MkYsL6Ve3NXkrNbO3vZOj8jDf7Y1kLxxIvAKwmqyos+S1HovlevNhSJO9eHisclpZj6PwMG/ra6/oPudFs7e8knVVv6mCjguPNoHOJWxxRuqpEAq6qOn5H1uKwWHycGJ1h8xKOb0BugNAxN84xNrNAVyoeSTFYs9mxtpuBrlRdB1I1gyiC4+/fs7VuleNBjGA34wZraf+mLcuSofPTvHbn0m4T4wnHuWn20piq8WbxC7vW8+iu65u9jNqpYh5H6LcU4cCf3kBbIsa56Vy7/npe4ptZsW+Fw9JSzKUzDE/MLenAOMCG3nYSMfFScoMt1S2tg9cdt86GgbmwR2VlehX7TbA4lp/dbFnSHByeRDXaORyNIBGPsWl1R55wLFeLY6kTRR2Hn7bIXFWmfiaSty+LtTgsTWVkco6HD5/jocNnefjIWZ4/PQnARetqazDYSmzt7/SEY3xmwXNfWVqTqFw+kQXHravKslLwC8VDh8/ywrAjFJ2pOLsH+3nXlZt4zUVr+LnNfc1daB3Y2t/Jt55y+nFai6N1MZfdqCyOeEyICWSDQyUWSa65pBUOyzKjnFBcNdjPu1+5iWsuHOAVm3qXXcbR1v5ORqcXGJtZsMLRwkgEwfEgqUSM2YXKI2erwUuDbsI/GysclrpyZmKOh4+cdYXiHAddoehyLYpffOVmrrmwn5cvQ6EIYlxTR0ammJ6PvqW6ZXFEGWROxh3hqFfLEci5qrTOlkwYrHBYFkU5obhqez//agUJRZCt7lyOp06MAdQ0w9wSPeZiHqnFEWGH3Pl0fS2ZMFjhsFTF8MRsnuvp0BlnRKoRive8ajPXXDjAyzeuIrHChCKIaa/+1NAosDzbjSwHos6qgmhSco2ras4Kh6XVqCQU7929xQpFCVa1J1ndmeSpE+POcyscLYkRjihdVaZ6vJ5GjRngNLuQqd+bhiRS4RCRG4D/jjMB8Auq+rHA69cB3wSOuJvuVNU/dV97EZgAMkBaVXe72/uBfwIGcSYA/pKqno/yPFYSw+OzPHQkJxSHXaHobktw1eBqfskVisusUIRia38n+086wrEULY4t/R1kG39D2xQimPDqEcX4WFPHsawsDhGJA5/BGf86BDwiInep6jOBXX+sqm8v8TZvUNWRwLbbge+p6sdE5Hb3+R/Uc+0riUpC8ctWKBbF1oEufjbkxjiWoHD8+D/+fLOX0ABM5fjSdFU1gygtjj3AQVU9DCAiXwFuBILCUS03Ate5j78E/BArHKE5PT7rBbIfPnyWwyM5odizvZ+brnKEYtcGKxT1YKtviuFSFI6VQCPScaOoHm/GrHFDlMKxCTjuez4EXF1kv1eLyM+Ak8Dvq+p+d7sC3xURBf6Hqn7O3b5eVU8BqOopEVlX7MNF5BbgFoCtW7cu+mSWKqWEoqctwVXb+7lpjxWKKPFXi1vhaG2WanC8GUQpHMV+C8GM48eAbao6KSJvBb4B7HRfu1ZVT7rCcK+IPKuq94X9cFdoPgewe/fuJmQ6N4ecUDhiccQnFHu29/O+PVsdodi4aknPu1gqmMyqzmXaUn054M3jsMIRmiiFYwjY4nu+Gceq8FDVcd/ju0XksyKyRlVHVPWku31YRL6O4/q6DzgtIhtca2MDMBzhObQ8L43N5tVRBIXi/VYomsq2gS7AWhutTKMqx53Pqt9nLFdX1SPAThHZDpwAbgLe799BRC4ATquqisgenG69Z0WkC4ip6oT7+E3An7qH3QXcDHzM/fnNCM+h5XhpLGdRPHzEJxTtCa7e3s+vXO0IxaUbrFC0AhesaicZFyscLYxXAGgtjtBEJhyqmhaR24Dv4KTjflFV94vIre7rdwDvAX5TRNLADHCTKyLrga+76pwA/lFV73Hf+mPAV0Xk14FjwHujOodW4NTYTF4dxYvuRDkrFEuDeEzYvLrT1nAsAaJsFhjF3HGTjtsMIv1kVb0buDuw7Q7f408Dny5y3GHg8hLveRZ4Y31X2jqcGptxROLQOR4+EhSKAX71mm1WKJYYt7zuwqbOh7aUxysAjPCfk2k5Us+PiGrORxhs5XiTOTk648QoDp3joSNnOeoKxar2BHusUCwL3rdn5Wb1LQWibqsO0biqmjH5z2CFo8GcHJ3Ji1H4heLqCwf4gBUKi6WhmIB1pFlVTbQOosAKR8T4heKhw+e8iXBGKD746kGuubCfl11ghcJiaSaN6I7bhJlLkWCFo86cGJ3h4SJC0duR5Ort/dy81wqFxdKKNKLJ4XLBCsciOTE6w0OHXKE4cpbj52aAnFD8672DXHPhAC+7oKepPkmLxVKcRtRxRNHkEJyZHM2YZW+Fo0qGzk/n0mN9QtHX6QjFr+3dboXCYlmCRFnYn4wgqwpg/5+8uSnuLyscFRg6P81DvjqKofP5QvFvrnWE4pL1VigslqWINKA7blSuqmZdc6xwlOH/+vpT/MPDx4CcUPz6a6xQWCzLiYa0HFlmfcqscJTh+l3r2bmum2t2DHDxOisUFstypJF1HPXsVdVMrHCU4bpL1sElzV6FxWJpBNG2HFleFsfyOhuLxWKpEs9VtcQqx5vJ8jobi8ViqZKGVI676bjLxFNlhcNisVhg6Y2ObSbL62wsFoulSnLB8eg+I6o6jmZhhcNisaxsvLbqNsYRluV1NhaLxVIlS3UCYDNZXmdjsVgsNRKlcHjpuMskOh6pcIjIDSLynIgcFJHbi7x+nYiMicgT7n8fdbdvEZEfiMgBEdkvIr/tO+aPReSE75i3RnkOFotleSMNcFXZyvGQiEgc+AxwPTAEPCIid6nqM4Fdf6yqbw9sSwO/p6qPiUgP8KiI3Os79pOq+hdRrd1isawcGlI5HsHM8WYSpQzuAQ6q6mFVnQe+AtwY5kBVPaWqj7mPJ4ADwKbIVmqxWFY8jbA4lot8RCkcm4DjvudDFL/4v1pEfiYi3xaRy4IvisggcCXwsG/zbSLypIh8UURWF/twEblFRPaJyL4zZ87UfhYWi2VZYyvHqyfKsyn2W9DA88eAbap6OfAp4Bt5byDSDXwN+Iiqjrub/xrYAVwBnAI+UezDVfVzqrpbVXevXbu21nOwWCzLnFxWVXSf0dWWQATak/HoPqSBRCkcQ8AW3/PNwEn/Dqo6rqqT7uO7gaSIrAEQkSSOaPyDqt7pO+a0qmZUNQt8HsclZrFYLDXRiOB4f1eKr/zGNbz95zZE9hmNJErheATYKSLbRSQF3ATc5d9BRC4Qt1GMiOxx13PW3fY3wAFV/cvAMf5v/t3A0xGeg8ViWSFE6aoCuPrCgWVjcUSWVaWqaRG5DfgOEAe+qKr7ReRW9/U7gPcAvykiaWAGuElVVUReA3wAeEpEnnDf8g9dq+TPReQKHLfXi8CHojoHi8Wy/DFyEaXFsdyIdB6He6G/O7DtDt/jTwOfLnLcTyiRgKCqH6jzMi0Wy0rGdMe1whGa5RXqt1gslhqJ2lW1nLDCYbFYVjSN6I673LBflcViWdE0IqtquWGFw2KxrGga0R13uWGFw2KxWLAWRzVY4bBYLCuaRrQcWW5Y4bBYLCuaRnTHXW5Y4bBYLBasq6oarHBYLJYVjXVVVY8VDovFsqJx2+URtxZHaKxwWCwWCxCzV8PQ2K/KYrFYsK6qarDCYbFYVjRejMO6qkJjhcNisaxoTOV4zFocobHCYbFYLFiLoxqscFgslhWN1+TQWhyhiVQ4ROQGEXlORA6KyO1FXr9ORMZE5An3v49WOlZE+kXkXhF5wf25OspzsFgsy5uYreOomsgmAIpIHPgMcD0wBDwiInep6jOBXX+sqm+v4tjbge+p6sdcQbkd+IOozsNisSxv3rTrAhYySndbpANRlxVRWhx7gIOqelhV54GvADfW4dgbgS+5j78EvKt+S7ZYLCuNwTVdfPgNFzV7GUuKKIVjE3Dc93zI3Rbk1SLyMxH5tohcFuLY9ap6CsD9ua7Yh4vILSKyT0T2nTlzZjHnYbFYLBYfUQpHMYehBp4/BmxT1cuBTwHfqOLYsqjq51R1t6ruXrt2bTWHWiwWi6UMUQrHELDF93wzcNK/g6qOq+qk+/huICkiayoce1pENgC4P4ejWb7FYrFYihGlcDwC7BSR7SKSAm4C7vLvICIXiNthTET2uOs5W+HYu4Cb3cc3A9+M8BwsFovFEiCyNAJVTYvIbcB3gDjwRVXdLyK3uq/fAbwH+E0RSQMzwE2qqkDRY923/hjwVRH5deAY8N6ozsFisVgshYhznV7e7N69W/ft29fsZVgsFsuSQkQeVdXdwe22ctxisVgsVWGFw2KxWCxVsSJcVSJyBjha4+FrgJE6LmcpYM955bASz9uec3i2qWpBPcOKEI7FICL7ivn4ljP2nFcOK/G87TkvHuuqslgsFktVWOGwWCwWS1VY4ajM55q9gCZgz3nlsBLP257zIrExDovFYrFUhbU4LBaLxVIVVjgsFovFUhVWOAg14lZE5K/c158UkVc2Y531JsR5/4p7vk+KyAMicnkz1llPKp2zb7+rRCQjIu9p5PqiIMw5u2OcnxCR/SLyo0avMQpC/H33isj/cucB7ReRX2vGOuuFiHxRRIZF5OkSr9fvOqaqK/o/nCaKh4ALgRTwM2BXYJ+3At/GmRNyDfBws9fdoPPeC6x2H79lqZ93mHP27fd94G7gPc1edwN+z33AM8BW9/m6Zq+7Qef9h8B/dR+vBc4BqWavfRHn/DrglcDTJV6v23XMWhzhRtzeCPydOjwE9JmZIEuYiuetqg+o6nn36UM4c1GWMmHHGf874Gssj1kvYc75/cCdqnoMQFVXynkr0OOOdujGEY50Y5dZP1T1PpxzKEXdrmNWOMKNuA07BncpUe05/TrO3cpSpuI5i8gm4N3AHQ1cV5SE+T1fDKwWkR+KyKMi8sGGrS46wpz3p4FLcYbEPQX8tqpmG7O8plC361hk8ziWEGHG1C56lG0LEvqcROQNOMLxmkhXFD1hzvm/AX+gqhl3xthSJ8w5J4BXAW8EOoAHReQhVX0+6sVFSJjzfjPwBPDzwA7gXhH5saqOR7y2ZlG365gVjhAjbkPus9QIdU4i8nPAF4C3qOrZBq0tKsKc827gK65orAHeKiJpVf1GQ1ZYf8L+fY+o6hQwJSL3AZcDS1k4wpz3rwEfUycAcFBEjgAvA37amCU2nLpdx6yrKsSIW/f5B92shGuAMVU91eiF1pkwo323AncCH1jid5+GiuesqttVdVBVB4H/CfzWEhYNCPf3/U3gtSKSEJFO4GrgQIPXWW/CnPcxHCsLEVkPXAIcbugqG0vdrmMr3uLQcCNu78bJSDgITOPcqSxpQp73R4EB4LPuHXhal3BX0ZDnvKwIc86qekBE7gGeBLLAF1S1aErnUiHk7/q/AH8rIk/huHH+QFWXbLt1EfkycB2wRkSGgP8bSEL9r2O25YjFYrFYqsK6qiwWi8VSFVY4LBaLxVIVVjgsFovFUhVWOCwWi8VSFVY4LBaLxVIVVjgslgYjIv9eRA6IyNdE5EERmROR32/2uiyWsKz4Og6LpQn8Fk634SlgG/Cupq7GYqkSa3FYLA1ERO7AafV9F/ArqvoIsNDcVVks1WEtDoulgajqrSJyA/CGpVylbFnZWIvDYrFYLFVhhcNisVgsVWGFw2KxWCxVYZscWiwNRkRexJn7kQD2AatwutJO4szFXq6DhCzLBCscFovFYqkK66qyWCwWS1VY4bBYLBZLVVjhsFgsFktVWOGwWCwWS1VY4bBYLBZLVVjhsFgsFktVWOGwWCwWS1X8H5bqIjAoyqu9AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lineplot(y=df_2d_jac['AUC'], x=df_2d_jac['f1'])" ] }, { "cell_type": "code", "execution_count": 501, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 501, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(y=df_2d_jac['AUC'], x=df_2d_jac['n_genes'])" ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 201.0)" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = sns.histplot(df_2d_jac['n_genes'], bins=10000)\n", "ax.set_xlim([0,201])" ] }, { "cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = sns.histplot(df_2d_jac['n_genes'],)" ] }, { "cell_type": "code", "execution_count": 1180, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 50\n", "1 200\n", "2 5000\n", "3 5000\n", "4 200\n", " ... \n", "75 20\n", "76 20\n", "77 500\n", "78 50\n", "79 500\n", "Name: n_genes, Length: 80, dtype: int64" ] }, "execution_count": 1180, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['n_genes']" ] }, { "cell_type": "code", "execution_count": 784, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 784, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(y=df_2d_jac['AUC'], x=df_2d_jac['f1'])" ] }, { "cell_type": "code", "execution_count": 524, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 524, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cell_type
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Non-Neuronal0.82083318.0331590.4950000.000998
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" ], "text/plain": [ " AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value\n", "cell_type \n", "GABAergic 0.756725 17.896457 0.430622 0.003177\n", "Glutamatergic 0.687831 18.717524 0.582011 0.052311\n", "Non-Neuronal 0.820833 18.033159 0.495000 0.000998" ] }, "execution_count": 370, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac" ] }, { "cell_type": "code", "execution_count": 311, "metadata": {}, "outputs": [], "source": [ "df_optimal_marker = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/optimal_number_markers.csv')\n", "\n", "\n", "df_optimal_marker.loc[df_optimal_marker.groupby('marker_set')['f1'].idxmax()]" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
cell_type
L2/3 IT_10.7335613506.4937960.4125961.739606e-55
L2/3 IT_20.6379853629.5799850.4394012.336410e-20
L2/3 IT_30.5351133733.9172550.4497375.855890e-05
L4/5 IT_10.6411273507.6383010.3723131.321543e-21
L4/5 IT_20.6453243592.4534240.4206761.643804e-22
...............
Vip Mybpc1_30.6528673576.5403680.4064257.652021e-25
Vip Serpinf1_10.5912553674.0551120.4272977.084149e-10
Vip Serpinf1_20.6906463617.2952800.4130613.120479e-37
Vip Serpinf1_30.6436373728.3020920.4500423.313931e-22
Vip Sncg0.6630673511.2664290.3970317.070980e-28
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86 rows × 4 columns

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" ], "text/plain": [ " AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value\n", "cell_type \n", "L2/3 IT_1 0.733561 3506.493796 0.412596 1.739606e-55\n", "L2/3 IT_2 0.637985 3629.579985 0.439401 2.336410e-20\n", "L2/3 IT_3 0.535113 3733.917255 0.449737 5.855890e-05\n", "L4/5 IT_1 0.641127 3507.638301 0.372313 1.321543e-21\n", "L4/5 IT_2 0.645324 3592.453424 0.420676 1.643804e-22\n", "... ... ... ... ...\n", "Vip Mybpc1_3 0.652867 3576.540368 0.406425 7.652021e-25\n", "Vip Serpinf1_1 0.591255 3674.055112 0.427297 7.084149e-10\n", "Vip Serpinf1_2 0.690646 3617.295280 0.413061 3.120479e-37\n", "Vip Serpinf1_3 0.643637 3728.302092 0.450042 3.313931e-22\n", "Vip Sncg 0.663067 3511.266429 0.397031 7.070980e-28\n", "\n", "[86 rows x 4 columns]" ] }, "execution_count": 213, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from hicmatrix import HiCMatrix as hm\n", "from hicmatrix.lib import MatrixFileHandler\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [], "source": [ "SRP_name='aggregates'\n", "resolution='10kbp_raw'\n", "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_human/{SRP_name}/{resolution}/max/spr/0/all_bins/KR_KR/hic_gene_corr_intra_nanranked.h5'\n", "\n", "jac_sim_intra = hm.hiCMatrix(exp_file_path)\n", "\n", "\n", "\n", "\n", "all_genes = [x[3].decode() for x in jac_sim_intra.cut_intervals]\n", "df_jac_corr_intra = pd.DataFrame(jac_sim_intra.matrix.toarray() , index=all_genes, columns = all_genes)\n" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "55410.54265398567" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jac_corr = df_jac_corr / df_jac_corr.max().max()" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [], "source": [ "df_jac_corr = df_jac_corr / df_jac_corr.max().max()" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [], "source": [ "df_jac_gw = df_jac_corr_intra + df_jac_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "resolution agg_type\n", "25 spearman 0.713044\n", "Name: AUC, dtype: float64\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "1 SRP249897 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "2 SRP292639 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "3 SRP217487 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "4 SRP075985 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "5 SRP105082 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "6 SRP110616 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "7 SRP118601 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "8 SRP200567 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "9 SRP223513 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "10 SRP218950 25\n", "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n", "11 SRP226118 25\n" ] } ], "source": [ " species_list = ['mouse']\n", " if species_list == ['human']:\n", " color_1 = '#98DBF6'\n", " color_2 = '#00A5E6'\n", " elif species_list == ['mouse']:\n", " color_1 = '#F69398'\n", " color_2 = '#E83B43'\n", " else:\n", " color_1 = '#E9DBC4'\n", " color_2 = '#EC9200' \n", " \n", " res_list = [25]\n", " #res_list = [10]\n", " #KR_type_list = ['KR_ranked_KR']\n", " KR_type_list = ['KR']\n", " #performance_type_list = ['all']\n", " edge_type_list = ['contact']\n", " #performance_type_list = ['inter_only/hic_gene_KR_inter_1_percent_per_chr.csv']\n", " performance_type_list = ['inter_only/hic_gene_corr_inter_excluding_intra_nanranked_1_percent_per_chr.csv']\n", " \n", " #f_name = 'hic_gene_corr_inter_1_percent_per_chr.csv'\n", " #f_name = 'hic_gene_KR_intra_1_percent_per_chr.csv'\n", " #f_name = 'hic_gene_corr_gw_1_percent_per_chr.csv'\n", "\n", " df_list = []\n", " for species in species_list: \n", " df_seq_depth = pd.read_csv(f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/stats/{species}_exclude_count_inter.txt', sep='\\t')\n", " df_seq_depth['species'] = species\n", " df_list.append(df_seq_depth)\n", "\n", " #whole aggreagte\n", " import pandas as pd\n", " empty_list = []\n", "\n", " for species in species_list: \n", " #for species in ['human']: \n", " #for bin_type in ['gene_bins', 'all_bins', 'non_gene']: \n", " for bin_type in ['all_bins']: \n", " for resolution in res_list:\n", " for coef in ['jac_sim']:\n", " #for coef in ['pcc', 'jac_sim']:\n", " #for mapping in ['tss']:\n", " for mapping in ['max']:\n", " #for percentile in [90]:\n", " for percentile in [90]:\n", " for gene_percentile in [1]:\n", " #for gene_percentile in [10]:\n", "\n", " for KR_type in KR_type_list:\n", " for performance_type, agg_type in zip(['/spr/0/all_bins/KR_KR/inter_only/hic_gene_corr_inter_excluding_intra_nanranked_1_percent_per_chr.csv', '/spr/0/all_bins/KR_KR/inter_only/hic_gene_corr_inter_excluding_intra_nanranked_ind_1_percent_per_chr.csv'], ['spearman']):\n", "\n", " file_path = f'/sonas-hs/gillis/hpc/data/lohia/hi_c_data_processing/data_{species}/aggregates/'\n", "\n", " file_name = f'{resolution}kbp_raw/{mapping}/{performance_type}'\n", " \n", " SRP_name = 'aggregates'\n", " \n", " exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/{SRP_name}/{resolution}kbp_raw/max/spr/0/all_bins/KR_KR/hic_gene_corr_inter_excluding_intra_nanranked.h5'\n", "\n", " jac_sim = hm.hiCMatrix(exp_file_path)\n", "\n", "\n", "\n", "\n", " all_genes = [x[3].decode() for x in jac_sim.cut_intervals]\n", " #df_jac_corr = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes)\n", " df_spr_corr = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes)\n", "\n", "\n", "\n", " try:\n", "\n", "\n", " df2, delo = run_egad(marker_table, df_spr_corr)\n", " df2['species'] = species\n", " df2['resolution'] = resolution\n", " df2['agg_type'] = agg_type\n", " df2['proj'] = 'all'\n", " df2['edge_type'] = ''\n", " empty_list.append(df2)\n", " except:\n", " continue\n", " df_whole_agg=pd.concat(empty_list) \n", " df_whole_agg['counts_inter'] = df_seq_depth['counts_inter'].sum()\n", " print (df_whole_agg.groupby(['resolution', 'agg_type'])['AUC'].mean())\n", " \n", " \n", " counter = 0\n", " performance_type_list = ['inter_only/hic_gene_corr_inter_excluding_intra_nanranked_1_percent_per_chr.csv']\n", " for species in species_list: \n", " contacts_path = f'/grid/gillis/data/nfox/hi_c_data_processing/data_{species}/stats/'\n", " df_con = pd.read_csv(f'{contacts_path}/project_network_counts.txt', sep=' ', names=['id', 'count'])\n", "\n", " import pandas as pd\n", " empty_list = []\n", "\n", " for proj in df_con['id'].tolist() :\n", " counter = counter + 1\n", " \n", "\n", " #for species in ['drosophila', 'drosophila', 'drosophila']: \n", "\n", " #for bin_type in ['gene_bins', 'all_bins', 'non_gene']: \n", " for bin_type in ['all_bins']: \n", " for resolution in res_list:\n", " for coef in ['jac_sim']:\n", " #for coef in ['pcc', 'jac_sim']:\n", " #for mapping in ['tss']:\n", " for mapping in ['max/spr/0/all_bins/KR_KR']:\n", " #print (proj)\n", " for percentile in ['90']:\n", " for KR_type in KR_type_list:\n", " for performance_type in performance_type_list:\n", " #for performance_type in ['intra_only', 'inter_only', 'all']:\n", " file_path = f'/sonas-hs/gillis/hpc/data/lohia/hi_c_data_processing/data_{species}/{proj}/'\n", " file_name = f'{resolution}kbp_raw/{mapping}/{performance_type}'\n", " \n", " SRP_name = proj\n", " \n", " exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/{SRP_name}/{resolution}kbp_raw/max/spr/0/all_bins/KR_KR/hic_gene_corr_inter_excluding_intra_nanranked.h5'\n", "\n", " jac_sim = hm.hiCMatrix(exp_file_path)\n", "\n", "\n", "\n", "\n", " all_genes = [x[3].decode() for x in jac_sim.cut_intervals]\n", " #df_jac_corr = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes)\n", " df_spr_corr = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes)\n", "\n", "\n", "\n", "\n", " #file_name = f'{resolution}kbp_raw/{mapping}/{coef}/{percentile}/{bin_type}/{KR_type}/{performance_type}/{f_name}'\n", "\n", " try:\n", "\n", " df2 = run_egad(marker_table, df_spr_corr)\n", " df2['species'] = species\n", " df2['resolution'] = resolution\n", " df2['proj'] = proj\n", " df2['agg_type'] = 'proj_agg'\n", " #print (proj)\n", " \n", " df_seq_depth_subset = df_seq_depth[df_seq_depth['proj_id'].isin([proj])]\n", " df2['counts_inter'] = df_seq_depth_subset['counts_inter'].sum()\n", "\n", "\n", " except:\n", " #print (f'{file_path}/{file_name}')\n", " print (counter, proj, resolution)\n", " \n", " continue\n", "\n", "\n", " empty_list.append(df2)\n", " df_ind=pd.concat(empty_list)\n", "\n", " df = pd.concat([df_whole_agg, df_ind])\n", "\n", " dt = df.groupby(['proj', 'resolution', 'agg_type'])['AUC', 'counts_inter'].mean().reset_index()\n", "\n", " for species in species_list: \n", " df_seq_depth = pd.read_csv(f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/stats/{species}_project_network_details.tsv', sep='\\t')\n", " df_seq_depth['species'] = species\n", " df_seq_depth['project'] = [x.strip() for x in df_seq_depth['project']]\n", " df_seq_depth['experiment type'] = [x.strip() for x in df_seq_depth['experiment type']]\n", " #dk['experiment type'] = [x if x=='Hi-C' else 'o' for x in dk['experiment type']]\n", "\n", " import numpy as np\n", " dt['log_contacts'] = [np.log10(x) for x in dt['counts_inter']]\n", "\n", " dt = dt.merge(df_seq_depth, left_on='proj', right_on='project')\n", " dk = pd.concat([dt, df_whole_agg.groupby(['proj', 'resolution', 'agg_type'])['AUC', 'counts_inter'].mean().reset_index()])\n", " dk['log_contacts'] = [np.log10(x) for x in dk['counts_inter']]\n", "\n", " dk['experiment type'] = [x if x=='Hi-C' else 'o' for x in dk['experiment type']]\n", " \n", " #dk = dk[dk['auc'] > 0.5]\n", " #dk = dk[dk['agg_type']=='proj_agg']\n", " #print (dk)\n", " \n", " \n", " \n", " \n", " import matplotlib.pyplot as plt\n", " sc_bar = sns.scatterplot(data=dk[dk['resolution']==25], x='log_contacts', y='AUC', style='agg_type', color=color_1,edgecolor=\"black\", s=100, linewidth=1.2, markers = {\"proj_agg\": \"*\", \"pearson\": \"o\", \"spearman\": \"^\"})\n", " sc_bar.legend().remove()\n", " #sc_bar = sns.scatterplot(data=dk[dk['resolution']==100], x='log_contacts', y='auc')\n", " #dk['log_contacts'] = dk['log_contacts'].round(0)\n", " #sc_bar = sns.lineplot(data=dk[dk['resolution']==10], x='log_contacts', y='auc')\n", " \n", " #sc_bar = sns.regplot(data=dk[dk['resolution']==10], x='log_contacts', y='auc')\n", " #sc_bar.set(ylim=(0.5, 0.67))\n", " plt.plot()\n", " #plt.savefig(f'/grid/gillis/data/lohia/hi_c_data_processing/notebooks/figures/{species_list}_ind_vs_agg_inter.pdf',\n", " # transparent=True)\n", " import matplotlib.pyplot as plt\n", " fig, ax = plt.subplots()\n", " #sns.scatterplot(x='resolution', y='auc', data=dt[dt['agg_type']=='all'], ax=ax)\n", " axb = sns.boxplot(x='resolution', y='AUC', data=dk[dk['agg_type']=='proj_agg'], ax=ax, color=color_1, orient=\"v\")\n", " #for i, patch in enumerate(axb.artists):\n", " # Boxes from left to right\n", "\n", " # patch.set_hatch('//')\n", " #sns.swarmplot(x='resolution', y='auc', data=dk[dk['agg_type']=='pearson'], ax=ax, size=10, color=color_2, orient=\"v\")\n", " sns.swarmplot(x='resolution', y='AUC', data=dk[dk['agg_type']=='spearman'], ax=ax, size=10, color=\"green\", orient=\"v\")\n", " #sns.lineplot(y='resolution', x='auc', data=dt[dt['agg_type']=='all'], ax=ax, color=\"green\")\n", " #ax.set(ylim=(0.5, 0.67))\n", " plt.plot()\n", " \n", " #plt.savefig(f'/grid/gillis/data/lohia/hi_c_data_processing/notebooks/figures/{species_list}_ind_vs_agg_all_resol_inter.pdf',\n", " # transparent=True)\n" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "No objects to concatenate", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_ind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mempty_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_whole_agg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_ind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'proj'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'resolution'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'agg_type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AUC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'counts_inter'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexes\u001b[0m \u001b[0mhave\u001b[0m \u001b[0moverlapping\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \"\"\"\n\u001b[0;32m--> 285\u001b[0;31m op = _Concatenator(\n\u001b[0m\u001b[1;32m 286\u001b[0m \u001b[0mobjs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No objects to concatenate\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" ] } ], "source": [ " df_ind=pd.concat(empty_list)\n", "\n", " df = pd.concat([df_whole_agg, df_ind])\n", "\n", " dt = df.groupby(['proj', 'resolution', 'agg_type'])['AUC', 'counts_inter'].mean().reset_index()\n" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "SRP_name='aggregates'\n", "#SRP_name='SRP217487'\n", "resolution='10kbp_raw'\n", "#df_jac_corr_list = []\n", "#for resolution in ['100kbp_raw', '250kbp_raw', '10', 40 , 25, snhic]:\n", "for resolution in ['10kbp_raw']:\n", " exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_mouse/{SRP_name}/{resolution}/max/spr/0/all_bins/KR_KR/hic_gene_corr_inter_excluding_intra_nanranked.h5'\n", "\n", " jac_sim = hm.hiCMatrix(exp_file_path)\n", "\n", "\n", "\n", "\n", " all_genes = [x[3].decode() for x in jac_sim.cut_intervals]\n", " #df_jac_corr = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes)\n", " df_jac_corr_list.append(pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_genes))\n", " \n", " df_2d_jac, go_chrom = run_egad(marker_table, df_jac_corr_list[7])\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "SRP_name='aggregates'\n", "resolution='40kbp_raw'\n", "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/software/CoCoCoNet/networks/human_prioAggNet.h5'\n", "\n", "jac_exp = hm.hiCMatrix(exp_file_path)\n", "all_genes = [x[3].decode() for x in jac_exp.cut_intervals]\n", "df_exp_corr = pd.DataFrame(jac_exp.matrix.toarray() , index=all_genes, columns = all_genes)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "57238269.755088426" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jac_corr.sum().sum()" ] }, { "cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n", " df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/scType_marker_genes.csv', header=1, usecols=[0,1,2,3], skipfooter=2)\n", ":7: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n", " df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/scType_marker_genes.csv', header=1, usecols=[0,1,2,3], skipfooter=2)\n" ] } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/scType_marker_genes.csv', header=1, usecols=[0,1,2,3], skipfooter=2)\n", "gitdf = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/ScTypeDB_full_github.tsv', sep='\\t')\n", "\n", "\n", "\n", "df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/scType_marker_genes.csv', header=1, usecols=[0,1,2,3], skipfooter=2)\n", "\n", "\n", "\n", "gitdf.rename(columns = {'tissueType':'Tissue', 'cellName':'Cell type',\n", " 'geneSymbolmore1':'Marker genes'}, inplace = True)\n", "\n", "df = pd.concat([gitdf, df])\n", "df = df[df['Tissue'].isin(['Brain', 'Adrenal'])]\n", "df['combined_type'] = df['Tissue'] + '-' + df['Cell type']\n", "df = df.drop_duplicates(subset='combined_type')\n", "#df['combined_type'] = df['Tissue'] \n", "#df['combined_type'] = df['Cell type']\n", "tissue_type_list = df['combined_type'].drop_duplicates().tolist()\n", "\n", "all_gene_list = df['Marker genes'].str.cat(sep=',').split(\",\")\n", "\n", "data_tissue = df.groupby(['combined_type']).apply(lambda grp: grp['Marker genes'].str.cat(sep=',').split(\",\"))\n", "\n", "all_gene_list = list(set(all_gene_list))\n", "\n", "nested_gene_marker_tissue_list = []\n", "df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/gene_name_ensg.txt', sep='\\t')\n", "df.dropna(inplace=True)\n", "dict_gene_name_to_ensg = df.set_index('Gene name').to_dict()['Gene stable ID']\n", "\n", "for i in tissue_type_list:\n", " \n", " gene_for_given_tissue = data_tissue[data_tissue.index ==i][0]\n", " #chrom_for_given_tisse = [dict_gene_name_to_ensg[x] if x in dict_gene_name_to_ensg.keys() else 'del' for x in all_gene_list]\n", " \n", " particular_gene_tissue = [ 1 if x in gene_for_given_tissue else 0 for x in all_gene_list]\n", " nested_gene_marker_tissue_list.append(particular_gene_tissue)\n", "\n", " \n", "\n", "all_gene_list = [dict_gene_name_to_ensg[x] if x in dict_gene_name_to_ensg.keys() else 'del' for x in all_gene_list ]\n", "marker_gene_table = pd.DataFrame(nested_gene_marker_tissue_list, columns = all_gene_list, index=tissue_type_list)\n" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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..................................................................
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Immune system-Plasma B cells0000000000...0000000000
..................................................................
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Teeth-Endothelial cells0000000001...0000000000
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ENSG00000183662 \\\n", "Immune system-Pro-B cells 0 ... 0 \n", "Immune system-Pre-B cells 0 ... 0 \n", "Immune system-Naive B cells 0 ... 0 \n", "Immune system-Memory B cells 0 ... 0 \n", "Immune system-Plasma B cells 0 ... 0 \n", "... ... ... ... \n", "Teeth-Odontoblasts 0 ... 0 \n", "Teeth-Endothelial cells 1 ... 0 \n", "Teeth-Immune cells 0 ... 0 \n", "Teeth-Glial cells 0 ... 0 \n", "Teeth-Epithelial cells 0 ... 0 \n", "\n", " ENSG00000172936 ENSG00000166573 \\\n", "Immune system-Pro-B cells 0 0 \n", "Immune system-Pre-B cells 0 0 \n", "Immune system-Naive B cells 0 0 \n", "Immune system-Memory B cells 0 0 \n", "Immune system-Plasma B cells 0 0 \n", "... ... ... \n", "Teeth-Odontoblasts 0 0 \n", "Teeth-Endothelial cells 0 0 \n", "Teeth-Immune cells 0 0 \n", "Teeth-Glial cells 0 0 \n", "Teeth-Epithelial cells 0 0 \n", "\n", " ENSG00000186472 ENSG00000108821 \\\n", "Immune system-Pro-B cells 0 0 \n", "Immune system-Pre-B cells 0 0 \n", "Immune system-Naive B cells 0 0 \n", "Immune system-Memory B cells 0 0 \n", "Immune system-Plasma B cells 0 0 \n", "... ... ... \n", "Teeth-Odontoblasts 0 1 \n", "Teeth-Endothelial cells 0 0 \n", "Teeth-Immune cells 0 0 \n", "Teeth-Glial cells 0 0 \n", "Teeth-Epithelial cells 0 0 \n", "\n", " ENSG00000196565 ENSG00000180440 del \\\n", "Immune system-Pro-B cells 0 0 0 \n", "Immune system-Pre-B cells 0 0 0 \n", "Immune system-Naive B cells 0 0 0 \n", "Immune system-Memory B cells 0 0 0 \n", "Immune system-Plasma B cells 0 0 0 \n", "... ... ... ... \n", "Teeth-Odontoblasts 0 0 0 \n", "Teeth-Endothelial cells 0 0 0 \n", "Teeth-Immune cells 0 0 0 \n", "Teeth-Glial cells 0 0 0 \n", "Teeth-Epithelial cells 0 0 0 \n", "\n", " ENSG00000145384 ENSG00000105929 \n", "Immune system-Pro-B cells 0 0 \n", "Immune system-Pre-B cells 0 0 \n", "Immune system-Naive B cells 0 0 \n", "Immune system-Memory B cells 0 0 \n", "Immune system-Plasma B cells 0 0 \n", "... ... ... \n", "Teeth-Odontoblasts 0 0 \n", "Teeth-Endothelial cells 0 0 \n", "Teeth-Immune cells 0 0 \n", "Teeth-Glial cells 0 0 \n", "Teeth-Epithelial cells 0 0 \n", "\n", "[324 rows x 3062 columns]" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_gene_table" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df_TF['ensg_gene'] = [dict_gene_name_to_ensg[x] if x in dict_gene_name_to_ensg.keys() else 'del' for x in df_TF['official gene symbol'] ]\n" ] } ], "source": [ "df_TF = gitdf[gitdf['species'] != 'Mm']\n", "df_TF['ensg_gene'] = [dict_gene_name_to_ensg[x] if x in dict_gene_name_to_ensg.keys() else 'del' for x in df_TF['official gene symbol'] ]\n", "\n", "\n", "\n", "df_TF = df_TF[df_TF['ensg_gene'] != 'del']\n", "\n", "\n", "\n", "df_TF['counter'] = 1\n", "\n", "df_TF_egad = df_TF.pivot_table(index=[\"ensg_gene\"], columns='germ layer', values='counter', aggfunc='max').T\n", "marker_gene_table = df_TF_egad.fillna(0)\n" ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'tissueType'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3079\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3081\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'tissueType'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'species'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'Mm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tissueType'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Brain'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'combined_type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'organ'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cell type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3022\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3023\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3025\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3026\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3080\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3081\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3084\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'tissueType'" ] } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/PanglaoDB_markers_27_Mar_2020.tsv', sep='\\t')\n", "df[df['species'] != 'Mm']\n", "\n", "df = df[df['tissueType'].isin(['Brain'])]\n", "df['combined_type'] = df['organ'] + '-' + df['cell type']\n", "\n", "#df['combined_type'] = df['Tissue'] \n", "#df['combined_type'] = df['Cell type']\n", "tissue_type_list = df['combined_type'].drop_duplicates().tolist()\n", "\n", "all_gene_list = df['official gene symbol'].str.cat(sep=',').split(\",\")\n", "\n", "data_tissue = df.groupby(['combined_type']).apply(lambda grp: grp['official gene symbol'].str.cat(sep=',').split(\",\"))\n", "\n", "all_gene_list = list(set(all_gene_list))\n", "\n", "nested_gene_marker_tissue_list = []\n", "\n", "for i in tissue_type_list:\n", " gene_for_given_tissue = data_tissue[data_tissue.index ==i][0]\n", " particular_gene_tissue = [ 1 if x in gene_for_given_tissue else 0 for x in all_gene_list]\n", " nested_gene_marker_tissue_list.append(particular_gene_tissue)\n", "\n", " \n", "df = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/gene_name_ensg.txt', sep='\\t')\n", "df.dropna(inplace=True)\n", "dict_gene_name_to_ensg = df.set_index('Gene name').to_dict()['Gene stable ID']\n", "all_gene_list = [dict_gene_name_to_ensg[x] if x in dict_gene_name_to_ensg.keys() else 'del' for x in all_gene_list ]\n", "marker_gene_table = pd.DataFrame(nested_gene_marker_tissue_list, columns = all_gene_list, index=tissue_type_list)\n" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "df = marker_gene_table.sum().reset_index()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "marker_gene_table = marker_gene_table.drop(df[df[0]>1]['index'].tolist(), axis = 1)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.histplot(marker_gene_table.sum())" ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(261, 261)\n", "(261, 36)\n", "0.9680715197956578\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":133: RuntimeWarning: invalid value encountered in true_divide\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 221, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_gene_table.T, df_jac_gw)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5595002913340569" ] }, "execution_count": 222, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Adrenal-Sympathoblasts0.249016270.2263900.4330710.018456
Brain-Oligodendrocytes0.284360272.8597790.5066930.008096
Brain-Neural Progenitor cells0.318399265.7509450.4256800.008982
Brain-Schwann precursor cells0.320261290.4861890.5986930.101839
Adrenal-SLC26A4_PAEP positive cells0.447707283.8951080.5520280.129433
Brain-Mature neurons0.458630276.3279110.4772730.371641
Brain-GABAergic neurons0.490196257.1731710.4307190.178585
Brain-Microglial cells0.491468269.3834930.4891980.330668
Brain-Immune system cells0.500000308.9304440.8130720.415369
Adrenal-Schwann cells0.521400264.9491910.4248120.396232
Adrenal-Lymphoid cells0.524682260.9011890.4357940.267279
Brain-Cancer stem cells0.535784282.4168990.5529410.409272
Brain-Glutamatergic neurons0.566710269.6997370.4797530.171519
Adrenal-Megakaryocytes0.577867259.7993910.4414550.042519
Brain-Astrocytes0.606127283.1873850.5542010.071724
Adrenal-Erythroblasts0.607067282.0100210.5981820.002765
Adrenal-Vascular endothelial cells0.622621288.4071450.6027890.090246
Brain-Radial glial cells0.646756259.6864790.4138180.045605
Adrenal-Stromal cells0.665788265.9890800.4066210.047102
Adrenal-Myeloid cells0.675911270.9704840.5425450.034683
Brain-Endothelial cells0.676420295.3259360.6489290.018947
Brain-Dopaminergic neurons0.697619272.0295010.4907410.055307
Adrenal-CSH1_CSH2 positive cells0.820580287.1183150.5193750.000020
Brain-Neuroepithelial cells0.828327279.2641910.5128460.004822
Brain-Oligodendrocyte precursor cells0.853813237.0955030.3156860.002726
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "Adrenal-Sympathoblasts 0.249016 270.226390 \n", "Brain-Oligodendrocytes 0.284360 272.859779 \n", "Brain-Neural Progenitor cells 0.318399 265.750945 \n", "Brain-Schwann precursor cells 0.320261 290.486189 \n", "Adrenal-SLC26A4_PAEP positive cells 0.447707 283.895108 \n", "Brain-Mature neurons 0.458630 276.327911 \n", "Brain-GABAergic neurons 0.490196 257.173171 \n", "Brain-Microglial cells 0.491468 269.383493 \n", "Brain-Immune system cells 0.500000 308.930444 \n", "Adrenal-Schwann cells 0.521400 264.949191 \n", "Adrenal-Lymphoid cells 0.524682 260.901189 \n", "Brain-Cancer stem cells 0.535784 282.416899 \n", "Brain-Glutamatergic neurons 0.566710 269.699737 \n", "Adrenal-Megakaryocytes 0.577867 259.799391 \n", "Brain-Astrocytes 0.606127 283.187385 \n", "Adrenal-Erythroblasts 0.607067 282.010021 \n", "Adrenal-Vascular endothelial cells 0.622621 288.407145 \n", "Brain-Radial glial cells 0.646756 259.686479 \n", "Adrenal-Stromal cells 0.665788 265.989080 \n", "Adrenal-Myeloid cells 0.675911 270.970484 \n", "Brain-Endothelial cells 0.676420 295.325936 \n", "Brain-Dopaminergic neurons 0.697619 272.029501 \n", "Adrenal-CSH1_CSH2 positive cells 0.820580 287.118315 \n", "Brain-Neuroepithelial cells 0.828327 279.264191 \n", "Brain-Oligodendrocyte precursor cells 0.853813 237.095503 \n", "\n", " DEGREE_NULL_AUC P_Value \n", "Adrenal-Sympathoblasts 0.433071 0.018456 \n", "Brain-Oligodendrocytes 0.506693 0.008096 \n", "Brain-Neural Progenitor cells 0.425680 0.008982 \n", "Brain-Schwann precursor cells 0.598693 0.101839 \n", "Adrenal-SLC26A4_PAEP positive cells 0.552028 0.129433 \n", "Brain-Mature neurons 0.477273 0.371641 \n", "Brain-GABAergic neurons 0.430719 0.178585 \n", "Brain-Microglial cells 0.489198 0.330668 \n", "Brain-Immune system cells 0.813072 0.415369 \n", "Adrenal-Schwann cells 0.424812 0.396232 \n", "Adrenal-Lymphoid cells 0.435794 0.267279 \n", "Brain-Cancer stem cells 0.552941 0.409272 \n", "Brain-Glutamatergic neurons 0.479753 0.171519 \n", "Adrenal-Megakaryocytes 0.441455 0.042519 \n", "Brain-Astrocytes 0.554201 0.071724 \n", "Adrenal-Erythroblasts 0.598182 0.002765 \n", "Adrenal-Vascular endothelial cells 0.602789 0.090246 \n", "Brain-Radial glial cells 0.413818 0.045605 \n", "Adrenal-Stromal cells 0.406621 0.047102 \n", "Adrenal-Myeloid cells 0.542545 0.034683 \n", "Brain-Endothelial cells 0.648929 0.018947 \n", "Brain-Dopaminergic neurons 0.490741 0.055307 \n", "Adrenal-CSH1_CSH2 positive cells 0.519375 0.000020 \n", "Brain-Neuroepithelial cells 0.512846 0.004822 \n", "Brain-Oligodendrocyte precursor cells 0.315686 0.002726 " ] }, "execution_count": 223, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values(by=['AUC']).tail(30)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2430, 2430)\n", "(2430, 324)\n", "0.9943377533912513\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":133: RuntimeWarning: invalid value encountered in true_divide\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_gene_table.T, df_jac_corr_intra)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Immune system-Neutrophils0.7535671233.0898700.5893500.000156
Placenta-IGFBP1_DKK1 positive cells0.7539501234.3481060.5859670.002217
Gastrointestinal tract-Enterochromaffin cells0.7649101222.9469820.5000830.001795
Immune system-Macrophages0.7708941200.9915140.3539400.012746
Intestine-Lymphoid cells0.7785931208.6346660.3940960.004614
Intestine-Erythroblasts0.7860541221.5150290.5832540.000067
Stomach-Myeloid cells0.8047271234.8663580.6109870.000033
Adrenal-Myeloid cells0.8239531217.8828200.5831110.001632
White adipose tissue-Mesothelial cells0.8304091190.2925870.2468000.000921
Adrenal-CSH1_CSH2 positive cells0.8834541213.1868400.4551210.000007
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "Immune system-Neutrophils 0.753567 1233.089870 \n", "Placenta-IGFBP1_DKK1 positive cells 0.753950 1234.348106 \n", "Gastrointestinal tract-Enterochromaffin cells 0.764910 1222.946982 \n", "Immune system-Macrophages 0.770894 1200.991514 \n", "Intestine-Lymphoid cells 0.778593 1208.634666 \n", "Intestine-Erythroblasts 0.786054 1221.515029 \n", "Stomach-Myeloid cells 0.804727 1234.866358 \n", "Adrenal-Myeloid cells 0.823953 1217.882820 \n", "White adipose tissue-Mesothelial cells 0.830409 1190.292587 \n", "Adrenal-CSH1_CSH2 positive cells 0.883454 1213.186840 \n", "\n", " DEGREE_NULL_AUC P_Value \n", "Immune system-Neutrophils 0.589350 0.000156 \n", "Placenta-IGFBP1_DKK1 positive cells 0.585967 0.002217 \n", "Gastrointestinal tract-Enterochromaffin cells 0.500083 0.001795 \n", "Immune system-Macrophages 0.353940 0.012746 \n", "Intestine-Lymphoid cells 0.394096 0.004614 \n", "Intestine-Erythroblasts 0.583254 0.000067 \n", "Stomach-Myeloid cells 0.610987 0.000033 \n", "Adrenal-Myeloid cells 0.583111 0.001632 \n", "White adipose tissue-Mesothelial cells 0.246800 0.000921 \n", "Adrenal-CSH1_CSH2 positive cells 0.455121 0.000007 " ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values(by=['AUC']).tail(10)" ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2430, 2430)\n", "(2430, 324)\n", "0.9943377533912513\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":133: RuntimeWarning: invalid value encountered in true_divide\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_gene_table.T, df_jac_corr)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
White adipose tissue-Adipocytes0.7918106.942758e+070.3534141.003347e-03
Brain-Neuroepithelial cells0.7997417.563654e+070.4231015.484859e-03
White adipose tissue-Smooth Muscle cells0.8077746.047817e+070.2569305.326387e-04
Adrenal-Stromal cells0.8255807.282575e+070.3816062.510403e-03
White adipose tissue-Lymphatic Endothelial cells0.8363796.743732e+070.2865302.986060e-07
White adipose tissue-Mesothelial cells0.8602746.058658e+070.1831652.220305e-04
Immune system-Eosinophils0.8744267.586037e+070.4062811.556932e-04
White adipose tissue-Endothelial cells0.8907396.223762e+070.2588826.014601e-07
White adipose tissue-Pericytes0.9165635.733133e+070.1524334.161103e-07
White adipose tissue-Endometrium0.9518315.453354e+070.1233805.749189e-08
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "White adipose tissue-Adipocytes 0.791810 6.942758e+07 \n", "Brain-Neuroepithelial cells 0.799741 7.563654e+07 \n", "White adipose tissue-Smooth Muscle cells 0.807774 6.047817e+07 \n", "Adrenal-Stromal cells 0.825580 7.282575e+07 \n", "White adipose tissue-Lymphatic Endothelial cells 0.836379 6.743732e+07 \n", "White adipose tissue-Mesothelial cells 0.860274 6.058658e+07 \n", "Immune system-Eosinophils 0.874426 7.586037e+07 \n", "White adipose tissue-Endothelial cells 0.890739 6.223762e+07 \n", "White adipose tissue-Pericytes 0.916563 5.733133e+07 \n", "White adipose tissue-Endometrium 0.951831 5.453354e+07 \n", "\n", " DEGREE_NULL_AUC \\\n", "White adipose tissue-Adipocytes 0.353414 \n", "Brain-Neuroepithelial cells 0.423101 \n", "White adipose tissue-Smooth Muscle cells 0.256930 \n", "Adrenal-Stromal cells 0.381606 \n", "White adipose tissue-Lymphatic Endothelial cells 0.286530 \n", "White adipose tissue-Mesothelial cells 0.183165 \n", "Immune system-Eosinophils 0.406281 \n", "White adipose tissue-Endothelial cells 0.258882 \n", "White adipose tissue-Pericytes 0.152433 \n", "White adipose tissue-Endometrium 0.123380 \n", "\n", " P_Value \n", "White adipose tissue-Adipocytes 1.003347e-03 \n", "Brain-Neuroepithelial cells 5.484859e-03 \n", "White adipose tissue-Smooth Muscle cells 5.326387e-04 \n", "Adrenal-Stromal cells 2.510403e-03 \n", "White adipose tissue-Lymphatic Endothelial cells 2.986060e-07 \n", "White adipose tissue-Mesothelial cells 2.220305e-04 \n", "Immune system-Eosinophils 1.556932e-04 \n", "White adipose tissue-Endothelial cells 6.014601e-07 \n", "White adipose tissue-Pericytes 4.161103e-07 \n", "White adipose tissue-Endometrium 5.749189e-08 " ] }, "execution_count": 184, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values(by=['AUC']).tail(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2118, 2118)\n", "(2118, 324)\n", "0.9941666958113291\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":133: RuntimeWarning: invalid value encountered in true_divide\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_exp, go_chrom = run_egad(marker_gene_table.T, df_exp_corr)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_exp, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_exp['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_exp['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [], "source": [ "df_2d_jac.reset_index(inplace=True)\n", "df_2d_exp.reset_index(inplace=True)\n", "coexp_contact = df_2d_jac.merge(df_2d_exp, left_on='index', right_on='index')" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Immune system-Basophils0.966514686.3318470.3968598.957349e-08
Immune system-Naive CD8+ T cells0.968314821.9508640.5151334.288808e-10
Immune system-γδ-T cells0.968605769.5530200.4643498.119871e-11
Immune system--T cells0.968899769.5530200.4643495.924728e-11
Placenta-Lymphoid cells0.969792713.8325400.4112462.444644e-08
Placenta-Myeloid cells0.973610674.4242800.3806198.182613e-09
Stomach-Lymphoid cells0.974136777.2571230.4673122.445967e-09
Placenta-Megakaryocytes0.975818715.1167470.4169964.939503e-07
Stomach-Squamous epithelial cells0.975893421.0263890.1491511.253213e-05
Heart-Lymphoid cells0.976310680.7265330.3680744.947886e-10
Immune system-Mast cells0.976431838.2486970.5455333.501668e-05
Intestine-Chromaffin cells0.978456351.1125870.0980113.959878e-05
Immune system-Eosinophils0.978604644.9422770.3433045.867062e-04
Adrenal-Erythroblasts0.980016422.4706340.1469792.215531e-06
Placenta-AFP_ALB positive cells0.982184433.9792870.1634721.149528e-07
Adrenal-Lymphoid cells0.983204745.4821430.4336824.165141e-09
Immune system-ISG expressing immune cells0.984182845.9371180.5406463.247684e-07
Intestine-Lymphoid cells0.984755729.6640590.4107821.164714e-06
Muscle-Erythroblasts0.992657456.6582360.1934688.335917e-09
Intestine-Erythroblasts0.997374395.9211330.1226227.362984e-09
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "Immune system-Basophils 0.966514 686.331847 \n", "Immune system-Naive CD8+ T cells 0.968314 821.950864 \n", "Immune system-γδ-T cells 0.968605 769.553020 \n", "Immune system--T cells 0.968899 769.553020 \n", "Placenta-Lymphoid cells 0.969792 713.832540 \n", "Placenta-Myeloid cells 0.973610 674.424280 \n", "Stomach-Lymphoid cells 0.974136 777.257123 \n", "Placenta-Megakaryocytes 0.975818 715.116747 \n", "Stomach-Squamous epithelial cells 0.975893 421.026389 \n", "Heart-Lymphoid cells 0.976310 680.726533 \n", "Immune system-Mast cells 0.976431 838.248697 \n", "Intestine-Chromaffin cells 0.978456 351.112587 \n", "Immune system-Eosinophils 0.978604 644.942277 \n", "Adrenal-Erythroblasts 0.980016 422.470634 \n", "Placenta-AFP_ALB positive cells 0.982184 433.979287 \n", "Adrenal-Lymphoid cells 0.983204 745.482143 \n", "Immune system-ISG expressing immune cells 0.984182 845.937118 \n", "Intestine-Lymphoid cells 0.984755 729.664059 \n", "Muscle-Erythroblasts 0.992657 456.658236 \n", "Intestine-Erythroblasts 0.997374 395.921133 \n", "\n", " DEGREE_NULL_AUC P_Value \n", "Immune system-Basophils 0.396859 8.957349e-08 \n", "Immune system-Naive CD8+ T cells 0.515133 4.288808e-10 \n", "Immune system-γδ-T cells 0.464349 8.119871e-11 \n", "Immune system--T cells 0.464349 5.924728e-11 \n", "Placenta-Lymphoid cells 0.411246 2.444644e-08 \n", "Placenta-Myeloid cells 0.380619 8.182613e-09 \n", "Stomach-Lymphoid cells 0.467312 2.445967e-09 \n", "Placenta-Megakaryocytes 0.416996 4.939503e-07 \n", "Stomach-Squamous epithelial cells 0.149151 1.253213e-05 \n", "Heart-Lymphoid cells 0.368074 4.947886e-10 \n", "Immune system-Mast cells 0.545533 3.501668e-05 \n", "Intestine-Chromaffin cells 0.098011 3.959878e-05 \n", "Immune system-Eosinophils 0.343304 5.867062e-04 \n", "Adrenal-Erythroblasts 0.146979 2.215531e-06 \n", "Placenta-AFP_ALB positive cells 0.163472 1.149528e-07 \n", "Adrenal-Lymphoid cells 0.433682 4.165141e-09 \n", "Immune system-ISG expressing immune cells 0.540646 3.247684e-07 \n", "Intestine-Lymphoid cells 0.410782 1.164714e-06 \n", "Muscle-Erythroblasts 0.193468 8.335917e-09 \n", "Intestine-Erythroblasts 0.122622 7.362984e-09 " ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_exp.sort_values(by=['AUC']).tail(20)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Adrenal-Erythroblasts0.7440157.830639e+070.4726691.577830e-02
Eye-GABAergic amacrine cells0.7452417.268417e+070.3507121.214904e-02
Stomach-PDE1C_ACSM3 positive cells0.7541327.301663e+070.3741271.073852e-03
Heart-Megakaryocytes0.7543747.470928e+070.4139421.688054e-03
Immune system-Intermediate monocytes0.7553598.918103e+070.6111742.788973e-03
Adrenal-SLC26A4_PAEP positive cells0.7578368.059366e+070.4965351.268912e-02
Eye-Glycinergic amacrine cells0.7584367.143455e+070.3319236.499352e-03
Immune system-Non-classical monocytes0.7652857.983960e+070.5097102.098806e-04
Adrenal-Stromal cells0.7660237.476892e+070.3798775.048967e-03
Spleen-Stromal cells0.7670476.456358e+070.3275213.439434e-03
White adipose tissue-Adipocytes0.7895247.134295e+070.3523231.522735e-03
Adrenal-CSH1_CSH2 positive cells0.8005418.125798e+070.4068558.298341e-05
Brain-Neuroepithelial cells0.8054717.778916e+070.4218122.012711e-03
Immune system-Eosinophils0.8178437.799499e+070.4043083.891490e-04
White adipose tissue-Mesothelial cells0.8422106.219281e+070.1815674.646414e-04
White adipose tissue-Lymphatic Endothelial cells0.8510036.926410e+070.2843521.587523e-07
White adipose tissue-Endothelial cells0.8536516.394237e+070.2583571.073930e-06
White adipose tissue-Adipose progenitor cells0.8796746.533446e+070.3143322.981767e-04
White adipose tissue-Pericytes0.9241435.884099e+070.1513232.336886e-07
White adipose tissue-Endometrium0.9558025.597243e+070.1224482.936266e-08
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" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "Adrenal-Erythroblasts 0.744015 7.830639e+07 \n", "Eye-GABAergic amacrine cells 0.745241 7.268417e+07 \n", "Stomach-PDE1C_ACSM3 positive cells 0.754132 7.301663e+07 \n", "Heart-Megakaryocytes 0.754374 7.470928e+07 \n", "Immune system-Intermediate monocytes 0.755359 8.918103e+07 \n", "Adrenal-SLC26A4_PAEP positive cells 0.757836 8.059366e+07 \n", "Eye-Glycinergic amacrine cells 0.758436 7.143455e+07 \n", "Immune system-Non-classical monocytes 0.765285 7.983960e+07 \n", "Adrenal-Stromal cells 0.766023 7.476892e+07 \n", "Spleen-Stromal cells 0.767047 6.456358e+07 \n", "White adipose tissue-Adipocytes 0.789524 7.134295e+07 \n", "Adrenal-CSH1_CSH2 positive cells 0.800541 8.125798e+07 \n", "Brain-Neuroepithelial cells 0.805471 7.778916e+07 \n", "Immune system-Eosinophils 0.817843 7.799499e+07 \n", "White adipose tissue-Mesothelial cells 0.842210 6.219281e+07 \n", "White adipose tissue-Lymphatic Endothelial cells 0.851003 6.926410e+07 \n", "White adipose tissue-Endothelial cells 0.853651 6.394237e+07 \n", "White adipose tissue-Adipose progenitor cells 0.879674 6.533446e+07 \n", "White adipose tissue-Pericytes 0.924143 5.884099e+07 \n", "White adipose tissue-Endometrium 0.955802 5.597243e+07 \n", "\n", " DEGREE_NULL_AUC \\\n", "Adrenal-Erythroblasts 0.472669 \n", "Eye-GABAergic amacrine cells 0.350712 \n", "Stomach-PDE1C_ACSM3 positive cells 0.374127 \n", "Heart-Megakaryocytes 0.413942 \n", "Immune system-Intermediate monocytes 0.611174 \n", "Adrenal-SLC26A4_PAEP positive cells 0.496535 \n", "Eye-Glycinergic amacrine cells 0.331923 \n", "Immune system-Non-classical monocytes 0.509710 \n", "Adrenal-Stromal cells 0.379877 \n", "Spleen-Stromal cells 0.327521 \n", "White adipose tissue-Adipocytes 0.352323 \n", "Adrenal-CSH1_CSH2 positive cells 0.406855 \n", "Brain-Neuroepithelial cells 0.421812 \n", "Immune system-Eosinophils 0.404308 \n", "White adipose tissue-Mesothelial cells 0.181567 \n", "White adipose tissue-Lymphatic Endothelial cells 0.284352 \n", "White adipose tissue-Endothelial cells 0.258357 \n", "White adipose tissue-Adipose progenitor cells 0.314332 \n", "White adipose tissue-Pericytes 0.151323 \n", "White adipose tissue-Endometrium 0.122448 \n", "\n", " P_Value \n", "Adrenal-Erythroblasts 1.577830e-02 \n", "Eye-GABAergic amacrine cells 1.214904e-02 \n", "Stomach-PDE1C_ACSM3 positive cells 1.073852e-03 \n", "Heart-Megakaryocytes 1.688054e-03 \n", "Immune system-Intermediate monocytes 2.788973e-03 \n", "Adrenal-SLC26A4_PAEP positive cells 1.268912e-02 \n", "Eye-Glycinergic amacrine cells 6.499352e-03 \n", "Immune system-Non-classical monocytes 2.098806e-04 \n", "Adrenal-Stromal cells 5.048967e-03 \n", "Spleen-Stromal cells 3.439434e-03 \n", "White adipose tissue-Adipocytes 1.522735e-03 \n", "Adrenal-CSH1_CSH2 positive cells 8.298341e-05 \n", "Brain-Neuroepithelial cells 2.012711e-03 \n", "Immune system-Eosinophils 3.891490e-04 \n", "White adipose tissue-Mesothelial cells 4.646414e-04 \n", "White adipose tissue-Lymphatic Endothelial cells 1.587523e-07 \n", "White adipose tissue-Endothelial cells 1.073930e-06 \n", "White adipose tissue-Adipose progenitor cells 2.981767e-04 \n", "White adipose tissue-Pericytes 2.336886e-07 \n", "White adipose tissue-Endometrium 2.936266e-08 " ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values(by=['AUC']).tail(20)" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [], "source": [ "y = marker_gene_table.T\n", "\n", "genes_intersect = y.index.intersection(df_jac_corr.index)\n", "\n", "nw = df_jac_corr.loc[genes_intersect, genes_intersect]\n", "\n", "marker_gene_table = marker_gene_table.loc[:, genes_intersect]\n", "\n", "species= marker_gene_table.T.idxmax(axis=1)\n", "\n", "lut = dict(zip(species.unique(), sns.color_palette(\"hls\", 20)))\n", "#lut = dict(zip(species.unique(), \"grrbrrryry\"))\n", "#lut = dict(zip(species.unique(), \"rrbb\"))\n", "#lut = dict(zip(['Brain-Astrocytes', 'Brain-Endothelial cells', 'Brain-Microglial cells','Brain-GABAergic neurons'], sns.color_palette(\"hls\", 4)))\n", "row_colors = species.map(lut)\n", "g = sns.clustermap(nw, row_colors=row_colors, row_cluster=True, metric=\"correlation\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "hicexp", "language": "python", "name": "hicexp" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }