{ "cells": [ { "cell_type": "code", "execution_count": null, "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=1, 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": null, "metadata": {}, "outputs": [], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 448, "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/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": 988, "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": 1106, "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_gw_KR_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_whole = 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))" ] }, { "cell_type": "code", "execution_count": 1340, "metadata": {}, "outputs": [], "source": [ "all_bins = [x[1] if x[3].decode() == \"non-gene\" else x[3].decode() for x in jac_sim.cut_intervals]" ] }, { "cell_type": "code", "execution_count": 1341, "metadata": {}, "outputs": [], "source": [ "df_max_gene_whole_by_bins = pd.DataFrame(jac_sim.matrix.toarray() , index=all_genes, columns = all_bins)" ] }, { "cell_type": "code", "execution_count": 1355, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1371, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1371, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"/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": 1436, "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": 1437, "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": 1438, "metadata": {}, "outputs": [], "source": [ "marker_list = pd.concat(marker_list_optimal_marker)" ] }, { "cell_type": "code", "execution_count": 1439, "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": 1313, "metadata": { "scrolled": true }, "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
..................................................................
294allNon-Neuronal96MAG60.648512165.82008116.747920456.9553700.370001...8908.8571437FalseTrueTrueTrueTrueTrueTrueENSMUSG00000036634
295allNon-Neuronal97FNBP160.6480285.5928860.955553454.9319250.063414...8908.8571437FalseTrueTrueTrueTrueTrueTrueENSMUSG00000075415
296allNon-Neuronal98FRMD4B60.6478059.0505472.335387192.1510030.124702...8908.8571437FalseTrueTrueTrueTrueTrueTrueENSMUSG00000030064
297allNon-Neuronal99PLLP60.64554061.88454812.642643190.0391320.314672...8908.8571437FalseTrueTrueTrueTrueTrueTrueENSMUSG00000031775
298allNon-Neuronal100PRR5L60.645414187.90618530.603359334.9964040.447383...8908.8571437FalseTrueTrueTrueTrueTrueTrueENSMUSG00000032841
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299 rows × 21 columns

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" ], "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", ".. ... ... ... ... ... ... ... \n", "294 all Non-Neuronal 96 MAG 6 0.648512 165.820081 \n", "295 all Non-Neuronal 97 FNBP1 6 0.648028 5.592886 \n", "296 all Non-Neuronal 98 FRMD4B 6 0.647805 9.050547 \n", "297 all Non-Neuronal 99 PLLP 6 0.645540 61.884548 \n", "298 all Non-Neuronal 100 PRR5L 6 0.645414 187.906185 \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", ".. ... ... ... ... ... \n", "294 16.747920 456.955370 0.370001 ... 8908.857143 \n", "295 0.955553 454.931925 0.063414 ... 8908.857143 \n", "296 2.335387 192.151003 0.124702 ... 8908.857143 \n", "297 12.642643 190.039132 0.314672 ... 8908.857143 \n", "298 30.603359 334.996404 0.447383 ... 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", ".. ... ... ... ... ... ... ... ... \n", "294 7 False True True True True True True \n", "295 7 False True True True True True True \n", "296 7 False True True True True True True \n", "297 7 False True True True True True True \n", "298 7 False 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", ".. ... \n", "294 ENSMUSG00000036634 \n", "295 ENSMUSG00000075415 \n", "296 ENSMUSG00000030064 \n", "297 ENSMUSG00000031775 \n", "298 ENSMUSG00000032841 \n", "\n", "[299 rows x 21 columns]" ] }, "execution_count": 1313, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_list" ] }, { "cell_type": "code", "execution_count": 1440, "metadata": {}, "outputs": [], "source": [ "marker_table = marker_list.pivot_table(index='gene_id', columns='cell_type', values='rank', aggfunc='sum')" ] }, { "cell_type": "code", "execution_count": 1441, "metadata": {}, "outputs": [], "source": [ "marker_table.fillna(0, inplace=True)" ] }, { "cell_type": "code", "execution_count": 1442, "metadata": {}, "outputs": [], "source": [ "marker_table[marker_table != 0] = 1" ] }, { "cell_type": "code", "execution_count": 1398, "metadata": {}, "outputs": [], "source": [ "gaba_100= marker_table[marker_table['GABAergic'] == 1].index.tolist()" ] }, { "cell_type": "code", "execution_count": 1319, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import mannwhitneyu" ] }, { "cell_type": "code", "execution_count": 1320, "metadata": {}, "outputs": [], "source": [ "males = [19, 22, 16, 29, 24]\n", "females = [20, 11, 17, 12]" ] }, { "cell_type": "code", "execution_count": 1375, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ENSMUSG00000062209 0.0\n", "ENSMUSG00000033007 0.0\n", "ENSMUSG00000036766 0.0\n", "ENSMUSG00000049866 0.0\n", "ENSMUSG00000040710 0.0\n", " ... \n", "ENSMUSG00000033278 0.0\n", "ENSMUSG00000096988 0.0\n", "ENSMUSG00000055471 0.0\n", "ENSMUSG00000024990 0.0\n", "ENSMUSG00000025207 0.0\n", "Name: 0, Length: 96, dtype: float32" ] }, "execution_count": 1375, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_max_gene_whole_group_1[0]" ] }, { "cell_type": "code", "execution_count": 1372, "metadata": {}, "outputs": [], "source": [ "U, p = mannwhitneyu(df_max_gene_whole_group_1['ENSMUSG00000102693'].tolist(), df_max_gene_whole_group_2['ENSMUSG00000102693'].tolist(), alternative=\"greater\")" ] }, { "cell_type": "code", "execution_count": 1422, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1423, "metadata": {}, "outputs": [], "source": [ " rank_abs = lambda x: stats.rankdata(np.abs(x))\n", " df_max_gene_whole_group_1_rank = np.apply_along_axis(rank_abs, 1, df_max_gene_whole_group_1)\n", " df_max_gene_whole_group_2_rank = np.apply_along_axis(rank_abs, 1, df_max_gene_whole_group_2)" ] }, { "cell_type": "code", "execution_count": 1421, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[152800. , 152781. , 155483. , ..., 3473.5, 3473.5, 3473.5],\n", " [145219. , 145945. , 151792. , ..., 5485. , 5485. , 5485. ],\n", " [151741. , 149008. , 152135. , ..., 3539. , 3539. , 3539. ],\n", " ...,\n", " [110107. , 51869. , 144514.5, ..., 3479. , 3479. , 3479. ],\n", " [ 36038. , 129833. , 152205. , ..., 7602. , 7602. , 7602. ],\n", " [ 27910. , 27382. , 150726.5, ..., 5072.5, 5072.5, 5072.5]])" ] }, "execution_count": 1421, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_max_gene_whole_group_1_rank" ] }, { "cell_type": "code", "execution_count": 1418, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ENSMUSG00000062209 1.502166e+06\n", "ENSMUSG00000033007 6.214633e+05\n", "ENSMUSG00000036766 8.634648e+05\n", "ENSMUSG00000049866 6.190533e+05\n", "ENSMUSG00000040710 8.145098e+05\n", " ... \n", "ENSMUSG00000033278 1.042847e+06\n", "ENSMUSG00000096988 5.000617e+05\n", "ENSMUSG00000055471 9.149840e+05\n", "ENSMUSG00000024990 4.045507e+05\n", "ENSMUSG00000025207 5.277368e+05\n", "Length: 96, dtype: float32" ] }, "execution_count": 1418, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_max_gene_whole_group_1.sum(axis=1)" ] }, { "cell_type": "code", "execution_count": 1419, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ENSMUSG00000102693 4.579685e+05\n", "ENSMUSG00000064842 4.478055e+05\n", "ENSMUSG00000051951 1.138781e+06\n", "ENSMUSG00000102851 3.812311e+05\n", "ENSMUSG00000103377 3.851205e+05\n", " ... \n", "ENSMUSG00000070263 4.051033e+05\n", "ENSMUSG00000094649 4.027944e+05\n", "ENSMUSG00000069475 4.109801e+05\n", "ENSMUSG00000059326 4.210461e+05\n", "ENSMUSG00000095993 4.267935e+05\n", "Length: 50129, dtype: float32" ] }, "execution_count": 1419, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_max_gene_whole_group_2.sum(axis=1)" ] }, { "cell_type": "code", "execution_count": 1410, "metadata": {}, "outputs": [], "source": [ "z_p_dict = []\n", "bin_id = []\n", "\n", "for column in df_max_gene_whole_group_1:\n", " bin_id.append(column)\n", " try:\n", " U, p_val = mannwhitneyu(df_max_gene_whole_group_1[column].tolist(), df_max_gene_whole_group_2[column].tolist(), alternative=\"greater\")\n", " z_p_dict.append(p_val)\n", " \n", " except:\n", " z_p_dict.append(np.nan)\n", " " ] }, { "cell_type": "code", "execution_count": 1412, "metadata": {}, "outputs": [], "source": [ "import statsmodels.api as sm\n", "auc_GO_terms_manw = pd.DataFrame(list(zip(bin_id, z_p_dict )), columns=['id', 'P_val_agg'])\n", "auc_GO_terms_manw['class'] = \"gaba\"\n", "auc_GO_terms_manw.dropna(subset=['P_val_agg'], inplace=True)\n", "p_val_adjusted = sm.stats.multipletests(auc_GO_terms_manw['P_val_agg'].values, method='fdr_bh')\n", "auc_GO_terms_manw['adjusted_P_val_agg'] = p_val_adjusted[1]" ] }, { "cell_type": "code", "execution_count": 1414, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idP_val_aggclassadjusted_P_val_agg
0ENSMUSG000001026935.513157e-09gaba7.200498e-09
1ENSMUSG000000648423.886191e-09gaba5.200199e-09
2ENSMUSG000000519511.444101e-05gaba1.476606e-05
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4ENSMUSG000001033774.873608e-11gaba1.070688e-10
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1564532462711.712848e-10gaba3.140524e-10
1564542462722.073563e-11gaba5.233576e-11
1564552462732.826440e-13gaba1.775684e-12
1564562462741.129499e-13gaba9.069900e-13
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" ], "text/plain": [ " id P_val_agg class adjusted_P_val_agg\n", "0 ENSMUSG00000102693 5.513157e-09 gaba 7.200498e-09\n", "1 ENSMUSG00000064842 3.886191e-09 gaba 5.200199e-09\n", "2 ENSMUSG00000051951 1.444101e-05 gaba 1.476606e-05\n", "3 ENSMUSG00000102851 8.447245e-10 gaba 1.293078e-09\n", "4 ENSMUSG00000103377 4.873608e-11 gaba 1.070688e-10\n", "... ... ... ... ...\n", "156452 246269 8.509144e-13 gaba 4.072821e-12\n", "156453 246271 1.712848e-10 gaba 3.140524e-10\n", "156454 246272 2.073563e-11 gaba 5.233576e-11\n", "156455 246273 2.826440e-13 gaba 1.775684e-12\n", "156456 246274 1.129499e-13 gaba 9.069900e-13\n", "\n", "[149759 rows x 4 columns]" ] }, "execution_count": 1414, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auc_GO_terms_manw[auc_GO_terms_manw[\"adjusted_P_val_agg\"] < 0.05]" ] }, { "cell_type": "code", "execution_count": 1415, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idP_val_aggclassadjusted_P_val_agg
0ENSMUSG000001026935.513157e-09gaba7.200498e-09
1ENSMUSG000000648423.886191e-09gaba5.200199e-09
2ENSMUSG000000519511.444101e-05gaba1.476606e-05
3ENSMUSG000001028518.447245e-10gaba1.293078e-09
4ENSMUSG000001033774.873608e-11gaba1.070688e-10
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1564522462698.509144e-13gaba4.072821e-12
1564532462711.712848e-10gaba3.140524e-10
1564542462722.073563e-11gaba5.233576e-11
1564552462732.826440e-13gaba1.775684e-12
1564562462741.129499e-13gaba9.069900e-13
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" ], "text/plain": [ " id P_val_agg class adjusted_P_val_agg\n", "0 ENSMUSG00000102693 5.513157e-09 gaba 7.200498e-09\n", "1 ENSMUSG00000064842 3.886191e-09 gaba 5.200199e-09\n", "2 ENSMUSG00000051951 1.444101e-05 gaba 1.476606e-05\n", "3 ENSMUSG00000102851 8.447245e-10 gaba 1.293078e-09\n", "4 ENSMUSG00000103377 4.873608e-11 gaba 1.070688e-10\n", "... ... ... ... ...\n", "156452 246269 8.509144e-13 gaba 4.072821e-12\n", "156453 246271 1.712848e-10 gaba 3.140524e-10\n", "156454 246272 2.073563e-11 gaba 5.233576e-11\n", "156455 246273 2.826440e-13 gaba 1.775684e-12\n", "156456 246274 1.129499e-13 gaba 9.069900e-13\n", "\n", "[150049 rows x 4 columns]" ] }, "execution_count": 1415, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auc_GO_terms_manw" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[-1*np.log10(x) for x in df_whole['adjusted_P_val_agg']]" ] }, { "cell_type": "code", "execution_count": 1443, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6387, 6387)\n", "(6387, 80)\n", "0.9595095506497573\n", "0.0\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 1443, "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_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": 1448, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
cell_type
Pvalb Kank40.7940593264.1837880.2522414.612579e-24
Sst Pvalb Calb20.7963733579.3864950.2312393.085595e-06
Sst Th_20.7974903209.2810560.2556202.932289e-12
Lamp5 Egln3_30.8033613144.6619220.2377862.663758e-48
Sncg Col14a10.8037973642.5030060.3155256.646905e-04
Sst Tac20.8065782948.8194450.1803932.545760e-13
Sst Crhr2_10.8080403249.2385530.2292087.150684e-25
L5/6 NP_10.8091423100.7315500.2262461.566970e-14
Pvalb Il1rapl20.8092293157.3876490.2209801.145872e-26
L5 PT_30.8155073511.2868980.3082819.287629e-27
Sst C1ql3_20.8247922904.2645080.1720567.641504e-16
Vip Chat_20.8299123254.3483000.2624974.105930e-15
Vip Igfbp6_20.8364752981.5588670.1852191.948949e-16
Sst Crhr2_20.8398932441.5391080.1728216.217317e-08
Sst C1ql3_10.8405083049.9113490.1704474.265060e-17
L5/6 NP CT0.8515643117.4384560.2865792.634787e-07
Lamp5 Pdlim5_20.8557583147.3685100.2263601.087074e-04
Lamp5 Pax60.8562323009.4009410.1797942.059688e-08
L5 IT_30.8645253085.3968910.1283056.640824e-05
Pvalb Reln0.8809352932.0437140.1870217.082721e-09
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value\n", "cell_type \n", "Pvalb Kank4 0.794059 3264.183788 0.252241 4.612579e-24\n", "Sst Pvalb Calb2 0.796373 3579.386495 0.231239 3.085595e-06\n", "Sst Th_2 0.797490 3209.281056 0.255620 2.932289e-12\n", "Lamp5 Egln3_3 0.803361 3144.661922 0.237786 2.663758e-48\n", "Sncg Col14a1 0.803797 3642.503006 0.315525 6.646905e-04\n", "Sst Tac2 0.806578 2948.819445 0.180393 2.545760e-13\n", "Sst Crhr2_1 0.808040 3249.238553 0.229208 7.150684e-25\n", "L5/6 NP_1 0.809142 3100.731550 0.226246 1.566970e-14\n", "Pvalb Il1rapl2 0.809229 3157.387649 0.220980 1.145872e-26\n", "L5 PT_3 0.815507 3511.286898 0.308281 9.287629e-27\n", "Sst C1ql3_2 0.824792 2904.264508 0.172056 7.641504e-16\n", "Vip Chat_2 0.829912 3254.348300 0.262497 4.105930e-15\n", "Vip Igfbp6_2 0.836475 2981.558867 0.185219 1.948949e-16\n", "Sst Crhr2_2 0.839893 2441.539108 0.172821 6.217317e-08\n", "Sst C1ql3_1 0.840508 3049.911349 0.170447 4.265060e-17\n", "L5/6 NP CT 0.851564 3117.438456 0.286579 2.634787e-07\n", "Lamp5 Pdlim5_2 0.855758 3147.368510 0.226360 1.087074e-04\n", "Lamp5 Pax6 0.856232 3009.400941 0.179794 2.059688e-08\n", "L5 IT_3 0.864525 3085.396891 0.128305 6.640824e-05\n", "Pvalb Reln 0.880935 2932.043714 0.187021 7.082721e-09" ] }, "execution_count": 1448, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values('AUC').tail(20)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "df_2d_jac" ] }, { "cell_type": "code", "execution_count": 1292, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ENSMUSG00000102693 0.003136\n", "ENSMUSG00000064842 0.002995\n", "ENSMUSG00000051951 0.000089\n", "ENSMUSG00000102851 0.003114\n", "ENSMUSG00000103377 0.002927\n", " ... \n", "ENSMUSG00000070263 0.000967\n", "ENSMUSG00000094649 0.001762\n", "ENSMUSG00000069475 0.000708\n", "ENSMUSG00000059326 0.002858\n", "ENSMUSG00000095993 0.001743\n", "Length: 50225, dtype: float64" ] }, "execution_count": 1292, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jac_corr.min()" ] }, { "cell_type": "code", "execution_count": 1280, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.910695886843628e-10" ] }, "execution_count": 1280, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jac_corr.min().min()" ] }, { "cell_type": "code", "execution_count": 1294, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'non-gene'", "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: 'non-gene'", "\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[0;32m----> 1\u001b[0;31m \u001b[0mdf_jac_corr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'non-gene'\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/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: 'non-gene'" ] } ], "source": [ "df_jac_corr['non-gene']" ] }, { "cell_type": "code", "execution_count": 1231, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6631469905200643" ] }, "execution_count": 1231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 1260, "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_whole_by_bins.index)\n", "#genes_intersect = marker_list.gene_id\n", "nw = (df_max_gene_whole_by_bins.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": 1267, "metadata": {}, "outputs": [], "source": [ "nw.to_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/nw_cluster_marker_vs_all_bin_index.csv_2.gz', sep='\\t')\n", "\n" ] }, { "cell_type": "code", "execution_count": 1266, "metadata": {}, "outputs": [], "source": [ "nw.std(axis=0).to_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/nw_cluster_marker_vs_all_bin_index_std.csv', sep='\\t')" ] }, { "cell_type": "code", "execution_count": 1264, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6387, 150041)" ] }, "execution_count": 1264, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.shape" ] }, { "cell_type": "code", "execution_count": 1242, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6387, 150041)" ] }, "execution_count": 1242, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.shape" ] }, { "cell_type": "code", "execution_count": 1243, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6387, 150041)" ] }, "execution_count": 1243, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw.shape" ] }, { "cell_type": "code", "execution_count": 1246, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/grid/gillis/home/lohia'" ] }, "execution_count": 1246, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwd" ] }, { "cell_type": "code", "execution_count": 1247, "metadata": {}, "outputs": [ { "ename": "IsADirectoryError", "evalue": "[Errno 21] Is a directory: '/grid/gillis/data/lohia/hi_c_data_processing/notebooks'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIsADirectoryError\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[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/grid/gillis/data/lohia/hi_c_data_processing/notebooks'\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/pandas/core/generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[1;32m 3385\u001b[0m )\n\u001b[1;32m 3386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3387\u001b[0;31m return DataFrameRenderer(formatter).to_csv(\n\u001b[0m\u001b[1;32m 3388\u001b[0m \u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3389\u001b[0m \u001b[0mline_terminator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline_terminator\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/io/formats/format.py\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[1;32m 1081\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1082\u001b[0m )\n\u001b[0;32m-> 1083\u001b[0;31m \u001b[0mcsv_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\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 1084\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1085\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcreated_buffer\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/io/formats/csvs.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 226\u001b[0m \"\"\"\n\u001b[1;32m 227\u001b[0m \u001b[0;31m# apply compression and byte/text conversion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m with get_handle(\n\u001b[0m\u001b[1;32m 229\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\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/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"replace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 641\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 642\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 643\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mIsADirectoryError\u001b[0m: [Errno 21] Is a directory: '/grid/gillis/data/lohia/hi_c_data_processing/notebooks'" ] } ], "source": [ "nw.to_csv('/grid/gillis/data/lohia/hi_c_data_processing/notebooks/nw_cluster_marker_vs_all_bin_index.csv', sep='\\t')" ] }, { "cell_type": "code", "execution_count": 1229, "metadata": {}, "outputs": [], "source": [ "nw = nw.loc[(nw.sum(axis=1) != 0), (nw.std(axis=0) <= 10)]\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": 1281, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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groupcell_typerankgenerecurrenceaurocfold_changefold_change_detectionexpressionprecisionrecallpopulation_sizen_datasetsscSSsnSSscCv2snCv2snCv3MscCv3snCv3Z
0GABAergicLamp51Cacna2d170.91814610.0423142.918454679.6201800.4218140.9201702037.8571437TrueTrueTrueTrueTrueTrueTrue
1000GABAergicPvalb1Pvalb70.90846351.89054310.259585548.7931870.7303630.8354002605.4285717TrueTrueTrueTrueTrueTrueTrue
2000GABAergicSncg1Cnr170.96823910.0290391.7912923250.9497380.0626760.977336397.5714297TrueTrueTrueTrueTrueTrueTrue
3000GABAergicSst1Sst70.951460131.6228436.2436202996.1600640.6746650.9205482647.4285717TrueTrueTrueTrueTrueTrueTrue
4000GABAergicVip1Vip70.92219744.3183315.7902945766.1370650.6320190.8810932499.8571437TrueTrueTrueTrueTrueTrueTrue
5000GlutamatergicL2/3 IT1Rasgrf270.8747158.5444933.443147250.6745310.4754820.82578012214.2857147TrueTrueTrueTrueTrueTrueTrue
6000GlutamatergicL5 ET1Gm216470.93260517.2737226.952110230.6247230.1592510.9120251374.2857147TrueTrueTrueTrueTrueTrueTrue
7000GlutamatergicL5 IT1Slc24a370.8150526.1945822.648420230.4926600.5841560.78808717987.0000007TrueTrueTrueTrueTrueTrueTrue
8000GlutamatergicL5/6 NP1Tshz270.988350166.75015311.8351382145.0512660.3620300.9794092103.0000007TrueTrueTrueTrueTrueTrueTrue
9000GlutamatergicL6 CT1Foxp270.94235923.1982305.332981447.8280750.6008980.91347310768.2857147TrueTrueTrueTrueTrueTrueTrue
10000GlutamatergicL6 IT1Galnt1470.8448256.7961503.412028254.1883080.2504780.8155864230.5714297TrueTrueTrueTrueTrueTrueTrue
11000GlutamatergicL6 IT Car31Synpr40.98591439.6841053.6528901800.6956110.0157980.983788253.2500004TrueNaNTrueTrueNaNTrueNaN
12000GlutamatergicL6b1Inpp4b70.93038225.6158015.404430600.0094930.1812910.9079251018.5714297TrueTrueTrueTrueTrueTrueTrue
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" ], "text/plain": [ " group cell_type rank gene recurrence auroc \\\n", "0 GABAergic Lamp5 1 Cacna2d1 7 0.918146 \n", "1000 GABAergic Pvalb 1 Pvalb 7 0.908463 \n", "2000 GABAergic Sncg 1 Cnr1 7 0.968239 \n", "3000 GABAergic Sst 1 Sst 7 0.951460 \n", "4000 GABAergic Vip 1 Vip 7 0.922197 \n", "5000 Glutamatergic L2/3 IT 1 Rasgrf2 7 0.874715 \n", "6000 Glutamatergic L5 ET 1 Gm2164 7 0.932605 \n", "7000 Glutamatergic L5 IT 1 Slc24a3 7 0.815052 \n", "8000 Glutamatergic L5/6 NP 1 Tshz2 7 0.988350 \n", "9000 Glutamatergic L6 CT 1 Foxp2 7 0.942359 \n", "10000 Glutamatergic L6 IT 1 Galnt14 7 0.844825 \n", "11000 Glutamatergic L6 IT Car3 1 Synpr 4 0.985914 \n", "12000 Glutamatergic L6b 1 Inpp4b 7 0.930382 \n", "\n", " fold_change fold_change_detection expression precision recall \\\n", "0 10.042314 2.918454 679.620180 0.421814 0.920170 \n", "1000 51.890543 10.259585 548.793187 0.730363 0.835400 \n", "2000 10.029039 1.791292 3250.949738 0.062676 0.977336 \n", "3000 131.622843 6.243620 2996.160064 0.674665 0.920548 \n", "4000 44.318331 5.790294 5766.137065 0.632019 0.881093 \n", "5000 8.544493 3.443147 250.674531 0.475482 0.825780 \n", "6000 17.273722 6.952110 230.624723 0.159251 0.912025 \n", "7000 6.194582 2.648420 230.492660 0.584156 0.788087 \n", "8000 166.750153 11.835138 2145.051266 0.362030 0.979409 \n", "9000 23.198230 5.332981 447.828075 0.600898 0.913473 \n", "10000 6.796150 3.412028 254.188308 0.250478 0.815586 \n", "11000 39.684105 3.652890 1800.695611 0.015798 0.983788 \n", "12000 25.615801 5.404430 600.009493 0.181291 0.907925 \n", "\n", " population_size n_datasets scSS snSS scCv2 snCv2 snCv3M scCv3 \\\n", "0 2037.857143 7 True True True True True True \n", "1000 2605.428571 7 True True True True True True \n", "2000 397.571429 7 True True True True True True \n", "3000 2647.428571 7 True True True True True True \n", "4000 2499.857143 7 True True True True True True \n", "5000 12214.285714 7 True True True True True True \n", "6000 1374.285714 7 True True True True True True \n", "7000 17987.000000 7 True True True True True True \n", "8000 2103.000000 7 True True True True True True \n", "9000 10768.285714 7 True True True True True True \n", "10000 4230.571429 7 True True True True True True \n", "11000 253.250000 4 True NaN True True NaN True \n", "12000 1018.571429 7 True True True True True True \n", "\n", " snCv3Z \n", "0 True \n", "1000 True \n", "2000 True \n", "3000 True \n", "4000 True \n", "5000 True \n", "6000 True \n", "7000 True \n", "8000 True \n", "9000 True \n", "10000 True \n", "11000 NaN \n", "12000 True " ] }, "execution_count": 1181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_class_marker" ] }, { "cell_type": "code", "execution_count": 1180, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cell_typeAUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
0L2/3 IT_10.6933523800.5333740.4015812.016587e-06
1L2/3 IT_20.7416363661.3710660.3442408.205662e-31
2L4/5 IT_10.5745684153.0983120.4407984.198951e-15
3L4/5 IT_20.5956874238.0475060.4927581.888820e-23
4L5 IT_20.6355564025.2504600.4212133.444528e-11
..................
75Vip Mybpc1_30.7363953604.4682610.2729621.928416e-04
76Vip Serpinf1_10.6623413707.0466560.2590752.523407e-03
77Vip Serpinf1_20.6809424017.1203470.4211308.909665e-42
78Vip Serpinf1_30.7784703593.5764110.3020894.392372e-11
79Vip Sncg0.6651953894.9742070.3941462.536440e-35
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80 rows × 5 columns

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" ], "text/plain": [ " cell_type AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value\n", "0 L2/3 IT_1 0.693352 3800.533374 0.401581 2.016587e-06\n", "1 L2/3 IT_2 0.741636 3661.371066 0.344240 8.205662e-31\n", "2 L4/5 IT_1 0.574568 4153.098312 0.440798 4.198951e-15\n", "3 L4/5 IT_2 0.595687 4238.047506 0.492758 1.888820e-23\n", "4 L5 IT_2 0.635556 4025.250460 0.421213 3.444528e-11\n", ".. ... ... ... ... ...\n", "75 Vip Mybpc1_3 0.736395 3604.468261 0.272962 1.928416e-04\n", "76 Vip Serpinf1_1 0.662341 3707.046656 0.259075 2.523407e-03\n", "77 Vip Serpinf1_2 0.680942 4017.120347 0.421130 8.909665e-42\n", "78 Vip Serpinf1_3 0.778470 3593.576411 0.302089 4.392372e-11\n", "79 Vip Sncg 0.665195 3894.974207 0.394146 2.536440e-35\n", "\n", "[80 rows x 5 columns]" ] }, "execution_count": 1180, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.reset_index()" ] }, { "cell_type": "code", "execution_count": 1248, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ENSMUSG00000102693ENSMUSG00000064842ENSMUSG00000051951ENSMUSG00000102851ENSMUSG00000103377ENSMUSG00000104017ENSMUSG00000103025ENSMUSG00000089699ENSMUSG00000103201ENSMUSG00000103147...non-genenon-genenon-genenon-genenon-genenon-genenon-genenon-genenon-genenon-gene
ENSMUSG000000000560.3679570.2557791.6418080.6308340.5592410.6694870.5605941.1545031.1038800.463620...1.7734251.9197942.4864750.9118572.5684712.8602963.7699733.8381453.1277021.228238
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..................................................................
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6387 rows × 150041 columns

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cell_typeAUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Valuegrouprankgenerecurrenceauroc...recallpopulation_sizen_datasetsscSSsnSSscCv2snCv2snCv3MscCv3snCv3Z
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0 rows × 24 columns

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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [cell_type, AUC, AVG_NODE_DEGREE, DEGREE_NULL_AUC, P_Value, group, rank, gene, recurrence, auroc, fold_change, fold_change_detection, expression, precision, recall, population_size, n_datasets, scSS, snSS, scCv2, snCv2, snCv3M, scCv3, snCv3Z]\n", "Index: []\n", "\n", "[0 rows x 24 columns]" ] }, "execution_count": 1179, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.reset_index().merge(df_class_marker)" ] }, { "cell_type": "code", "execution_count": 1121, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cell_type
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 1121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_table" ] }, { "cell_type": "code", "execution_count": 1134, "metadata": {}, "outputs": [], "source": [ "y = marker_table\n", "#y = y.sort_values(by=['GABAergic', 'Glutamatergic', 'Non-Neuronal'])\n", "genes_intersect = y.index.intersection(df_max_gene_whole.index)\n", "#genes_intersect = marker_list.gene_id\n", "nw = (df_max_gene_whole.loc[genes_intersect, 'non-gene'])\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" ] }, { "cell_type": "code", "execution_count": 1135, "metadata": {}, "outputs": [], "source": [ "nw = nw.loc[(nw.sum(axis=1) != 0), (nw.sum(axis=0) != 0)]" ] }, { "cell_type": "code", "execution_count": 1136, "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, 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
..................................................................
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
\n", "

836 rows × 21 columns

\n", "
" ], "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": 975, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(526, 526)\n", "(526, 85)\n", "0.981816148512637\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": 975, "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_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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WXZyFFeBqYHNVfXxIn19u+pHkVHp1PjW5KiVJe9LFWVinA78D3J9kY9P274GjAKpqDXA+8IEku4AfAyurqjqoVZI0xMQDpKq+BWQPfa4ErpxMRZKkNvwmuiSpFQNEktSKASJJasUAkSS1YoBIkloxQCRJrRggkqRWDBBJUisGiCSpFQNEktSKASJJasUAkSS1YoBIkloxQCRJrRggkqRWDBBJUitd3RP97CQPJdmS5PIBy5Pkk83y+5Kc3EWdkqThurgn+iLgKmA5cBywKslxM7otB5Y2j9XApydapCRpj7rYAzkV2FJVj1TV88CNwHkz+pwHfK56vg0cmOTQSRcqSRpu4vdEBw4HHu+b3wq8aYQ+hwPbZ24syWp6eykAP0myaW8LvOmivd3CrNtYDDy59z9h7BbfdNHCqJP5P56LgSfn4n01ZgtmLLsuYgQLqc7Xt125iwDJgLZq0afXWLUWWAuQZLqqlu1deeO1EGoE65xLC6FGWBh1LoQaYcHVuaTt+l0cwtoKHNk3fwSwrUUfSVKHugiQe4ClSY5Osh+wErhlRp9bgPc1Z2O9GXi6ql5y+EqS1J2JH8Kqql1JLgVuBRYB11TVA0kubpavAdYBK4AtwHPA7464+bVjKHmuLYQawTrn0kKoERZGnQuhRvgZqTNVAz9akCRpVn4TXZLUigEiSWrlFREge7o0SleSHJnkziSbkzyQ5INN+0eTfD/JxuaxouM6H01yf1PLdNN2cJLbkzzcPB/UcY3H9o3XxiTPJLlsPoxlkmuS7Oj/DtJs45fkw8179aEk/7TDGv88yYPN5YJuTnJg074kyY/7xnTNJGqcpc6hr3EXYzlLnTf11fhoko1NeyfjOcvfn7l7b1bVgn7Q+yD+e8AxwH7AvcBxXdfV1HYocHIzfQDw9/Qu3/JR4N92XV9fnY8Ci2e0/Sfg8mb6cuDPuq5zxmv+v+h9AarzsQTeCpwMbNrT+DWv/73Aq4Gjm/fuoo5qfAewTzP9Z301LunvNw/GcuBr3NVYDqtzxvKPAR/pcjxn+fszZ+/NV8IeyCiXRulEVW2vqg3N9LPAZnrfqF8IzgOua6avA97ZXSkvcSbwvap6rOtCAKrqLuAHM5qHjd95wI1V9ZOq+gd6Zxqe2kWNVXVbVe1qZr9N7/tWnRoylsN0MpYwe51JArwHuGEStQwzy9+fOXtvvhICZNhlT+aVJEuAk4DvNE2XNocOrun68BC9b/nflmR9c2kYgEOq+e5N8/y6zqp7qZW8+B/nfBrL3YaN33x9v/4e8PW++aOT/M8k30zylq6K6jPoNZ6vY/kW4ImqerivrdPxnPH3Z87em6+EABn5siddSbI/8CXgsqp6ht7VhX8FOJHe9b0+1l11AJxeVSfTuwryJUne2nE9Q6X35dNzgS82TfNtLPdk3r1fk1wB7AKub5q2A0dV1UnAh4DPJ/nFrupj+Gs878aysYoX/wen0/Ec8PdnaNcBbbOO5yshQOb1ZU+S7Evvxbu+qr4MUFVPVNULVfWPwGeY0G73MFW1rXneAdzc1PNEmisgN887uqvwRZYDG6rqCZh/Y9ln2PjNq/drkguAc4D3VnMgvDmE8VQzvZ7esfBf7arGWV7jeTWWAEn2Ad4N3LS7rcvxHPT3hzl8b74SAmSUS6N0ojkWejWwuao+3tfef2n6dwF7fQXhtpK8JskBu6fpfbC6id4YXtB0uwD4ajcVvsSL/nc3n8ZyhmHjdwuwMsmrkxxN75433+2gPpKcDfwxcG5VPdfXPpXefXtIckxT4yNd1NjUMOw1njdj2ee3gAerauvuhq7Gc9jfH+byvTnpMwPGdLbBCnpnGHwPuKLrevrq+k16u4D3ARubxwrgr4D7m/ZbgEM7rPEYemde3As8sHv8gF8C7gAebp4Pngfj+QvAU8Br+9o6H0t6gbYd+L/0/hd34WzjB1zRvFcfApZ3WOMWese8d7831zR9/3nzXrgX2AD8s47Hcuhr3MVYDquzab8WuHhG307Gc5a/P3P23vRSJpKkVl4Jh7AkSR0wQCRJrRggkqRWDBBJUisGiCSpFQNEktSKASJJasUAkQZo7uGwOclnmnsp3Jbk54f0PaW50N/dzT02NjXti5r5e5rlFzXtZyT5myT/Lb37cVzffGuYJL/eXHBvfZJb+y458a+T/F2znRsnNQ7SbAwQabilwFVVdTzwQ3rfKB7kL+l9+/g04IW+9guBp6vqFOAU4PebS0RA78qol9G7B8MxwOnNdYs+BZxfVb8OXAP8x6b/5cBJVfVG4OK5+fWkvbNP1wVI89g/VNXGZno9vRsDvUh6d/E7oKr+R9P0eXoXJ4TedcXemOT8Zv619ELpeeC71Vwvqblz3RJ6IfUG4PZmh2QRvctlQO9yFNcn+Qrwlb3+zaQ5YIBIw/2kb/oFYNAhrEGXwO5f9gdVdeuLGpMzBmx7n6b/A82ezEy/Te8ueOcCf5Lk+PrpzaCkTngIS9oLVfW/gWeTvLlpWtm3+FbgA82hKZL8anPF42EeAqaSnNb03zfJ8UleBRxZVXcC/w44ENh/jn8V6WVzD0TaexcCn0nyf4C/AZ5u2j9L79DUhuZD8p3Mcmvgqnq+Odz1ySSvpffv8xP0rjT9X5u2AH9RVT8cxy8ivRxejVfaS0n2r6ofNdOX07vc+Ac7LksaO/dApL3320k+TO/f02PA+7stR5oM90CkESW5Cjh9RvN/qaq/7KIeqWsGiCSpFc/CkiS1YoBIkloxQCRJrRggkqRW/h859wtO/nKiCQAAAABJRU5ErkJggg==\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": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEHCAYAAABGNUbLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAASS0lEQVR4nO3df7DldX3f8edLFsSIylIW5la0K1PqBJ1U9OKIZDLEDcao49JUKE5NNi1mm6a1GpsfS5xOpn90hjZOSpI6pptVs42oEKJZQk2QrmCaaQbZVWpQoBhF3LphLwrxR2dCIO/+cT4rh+XcH+ze7zns+TwfM3fO9/v5fr/n+/7cu/u63/s53/M5qSokSX15xqwLkCRNn+EvSR0y/CWpQ4a/JHXI8JekDhn+ktShDUM+eZJTgV3AS4EC/jlwD3AtsBm4D7isqh5a6XlOP/302rx584CVStL82b9//4NVtWnStgx5n3+S3cD/rKpdSU4Cvg/4ZeCbVXVVkh3Axqr6pZWeZ3Fxsfbt2zdYnZI0j5Lsr6rFSdsGG/ZJ8lzgh4D3A1TVI1X1MLAV2N122w1cMlQNkqTJhhzzPxtYAj6Y5HNJdiV5NnBmVR0EaI9nDFiDJGmCIcN/A/By4H1VdR7wXWDHWg9Osj3JviT7lpaWhqpRkro0ZPgfAA5U1W1t/XpGvwweSLIA0B4PTTq4qnZW1WJVLW7aNPH1CknSURos/KvqL4GvJXlxa9oCfBG4AdjW2rYBe4aqQZI02aC3egJvB65pd/p8GfhnjH7hXJfkCuB+4NKBa5AkHWHQ8K+qO4BJtxltGfK8kqSV+Q5fSeqQ4S9JHRp6zH/mLt/2Ng4+OHn2iIXTN/LR3bumXJEkzd7ch//BBx9iYevPT9625z1TrkaSnh4c9pGkDhn+ktQhw1+SOmT4S1KHDH9J6pDhL0kdMvwlqUOGvyR1yPCXpA4Z/pLUIcNfkjpk+EtShwx/SeqQ4S9JHTL8JalDhr8kdcjwl6QOGf6S1CHDX5I6ZPhLUocMf0nqkOEvSR0y/CWpQxuGfPIk9wHfBh4DHq2qxSSnAdcCm4H7gMuq6qEh65AkPdE0rvx/uKpeVlWLbX0HsLeqzgH2tnVJ0hTNYthnK7C7Le8GLplBDZLUtaHDv4BPJtmfZHtrO7OqDgK0xzMmHZhke5J9SfYtLS0NXKYk9WXQMX/gwqr6epIzgJuT3L3WA6tqJ7ATYHFxsYYqUJJ6NOiVf1V9vT0eAj4OvBJ4IMkCQHs8NGQNkqQnGyz8kzw7yXMOLwOvBe4EbgC2td22AXuGqkGSNNmQwz5nAh9Pcvg8H66qP05yO3BdkiuA+4FLB6xBkjTBYOFfVV8G/uGE9m8AW4Y6ryRpdb7DV5I6ZPhLUocMf0nqkOEvSR0y/CWpQ4a/JHXI8JekDhn+ktQhw1+SOmT4S1KHDH9J6pDhL0kdMvwlqUOGvyR1yPCXpA4Z/pLUIcNfkjpk+EtShwx/SeqQ4S9JHTL8JalDhr8kdcjwl6QOGf6S1CHDX5I6ZPhLUocMf0nq0ODhn+SEJJ9LcmNbPy3JzUnubY8bh65BkvRE07jyfwdw19j6DmBvVZ0D7G3rkqQpGjT8k5wFvAHYNda8FdjdlncDlwxZgyTpyYa+8r8a+EXgb8fazqyqgwDt8YxJBybZnmRfkn1LS0sDlylJfRks/JO8EThUVfuP5viq2llVi1W1uGnTpnWuTpL6tmHA574QeFOS1wMnA89N8iHggSQLVXUwyQJwaMAaJEkTDHblX1VXVtVZVbUZuBz4VFW9FbgB2NZ22wbsGaoGSdJks7jP/yrg4iT3Ahe3dUnSFA057PM9VXUrcGtb/gawZRrnlSRN5jt8JalDhr8kdcjwl6QOGf6S1CHDX5I6ZPhLUocMf0nqkOEvSR0y/CWpQ4a/JHXI8JekDhn+ktQhw1+SOmT4S1KHDH9J6tCawj/JhWtpkyQdH9Z65f+ba2yTJB0HVvwkryQXAK8GNiV519im5wInDFmYJGk4q32M40nAKW2/54y1fwt481BFSZKGtWL4V9WngU8n+Z2q+uqUapIkDWytH+D+zCQ7gc3jx1TVa4YoSpI0rLWG/+8BvwXsAh4brhxJ0jSsNfwfrar3DVqJJGlq1nqr5x8m+dkkC0lOO/w1aGWSpMGs9cp/W3v8hbG2As5e33IkSdOwpvCvqhcNXYgkaXrWFP5JfnJSe1X9txWOORn4E+CZ7TzXV9WvtOGiaxndOXQfcFlVPfTUypYkHYu1DvucP7Z8MrAF+CywbPgDfw28pqq+k+RE4E+T/BHw48DeqroqyQ5gB/BLT710SdLRWuuwz9vH15M8D/jdVY4p4Dtt9cT2VcBW4KLWvhu4FcNfkqbqaKd0/n/AOavtlOSEJHcAh4Cbq+o24MyqOgjQHs84yhokSUdprWP+f8joqh1GE7p9P3DdasdV1WPAy5KcCnw8yUvXWliS7cB2gBe+8IVrPUyStAZrHfN/z9jyo8BXq+rAWk9SVQ8nuRV4HfBAkoWqOphkgdFfBZOO2QnsBFhcXKxJ+0iSjs6ahn3aBG93M5rZcyPwyGrHJNnUrvhJ8izgR9pz3MDj7xvYBux5ylVLko7JWj/J6zLgM8ClwGXAbUlWm9J5AbglyeeB2xmN+d8IXAVcnORe4OK2LkmaorUO+7wbOL+qDsHoqh74H8D1yx1QVZ8HzpvQ/g1Gt4pKkmZkrXf7PONw8DffeArHSpKeZtZ65f/HSW4CPtLW/wnwiWFKkiQNbbXP8P37jO7L/4UkPw78IBDgz4BrplCfJGkAqw3dXA18G6CqPlZV76qqn2N01X/1sKVJkoayWvhvbi/cPkFV7WM0MZsk6Ti0WvifvMK2Z61nIZKk6Vkt/G9P8tNHNia5Atg/TEmSpKGtdrfPOxnNyfNPeTzsF4GTgH80YF2SpAGtGP5V9QDw6iQ/DByelO2/V9WnBq9MkjSYtc7nfwtwy8C1SJKmxHfpSlKHDH9J6pDhL0kdMvwlqUOGvyR1yPCXpA4Z/pLUIcNfkjpk+EtShwx/SeqQ4S9JHTL8JalDhr8kdcjwl6QOGf6S1CHDX5I6ZPhLUocGC/8kL0hyS5K7knwhyTta+2lJbk5yb3vcOFQNkqTJhrzyfxT4t1X1/cCrgH+V5FxgB7C3qs4B9rZ1SdIUDRb+VXWwqj7blr8N3AU8H9gK7G677QYuGaoGSdJkUxnzT7IZOA+4DTizqg7C6BcEcMYyx2xPsi/JvqWlpWmUKUndGDz8k5wC/D7wzqr61lqPq6qdVbVYVYubNm0arkBJ6tCg4Z/kREbBf01Vfaw1P5BkoW1fAA4NWYMk6cmGvNsnwPuBu6rq18Y23QBsa8vbgD1D1SBJmmzDgM99IfATwJ8nuaO1/TJwFXBdkiuA+4FLB6xBkjTBYOFfVX8KZJnNW4Y6ryRpdb7DV5I6ZPhLUocMf0nqkOEvSR0y/CWpQ4a/JHXI8JekDhn+ktQhw1+SOmT4S1KHDH9J6pDhL0kdMvwlqUOGvyR1yPCXpA4Z/pLUIcNfkjpk+EtShwx/SeqQ4S9JHTL8JalDhr8kdcjwl6QOGf6S1CHDX5I6ZPhLUocMf0nq0GDhn+QDSQ4luXOs7bQkNye5tz1uHOr8kqTlDXnl/zvA645o2wHsrapzgL1tXZI0ZYOFf1X9CfDNI5q3Arvb8m7gkqHOL0la3rTH/M+sqoMA7fGM5XZMsj3JviT7lpaWplagJPXgafuCb1XtrKrFqlrctGnTrMuRpLky7fB/IMkCQHs8NOXzS5KYfvjfAGxry9uAPVM+vyQJ2DDUEyf5CHARcHqSA8CvAFcB1yW5ArgfuHSo8x+Ly7e9jYMPPvSk9oXTN/LR3btmUJEkra/Bwr+q3rLMpi1DnXO9HHzwIRa2/vyT2/e8ZwbVSNL6e9q+4CtJGo7hL0kdMvwlqUOGvyR1yPCXpA4Z/pLUIcNfkjpk+EtShwx/SeqQ4S9JHTL8JalDhr8kdWiwid00PcvNQgrORCppMsN/Diw3Cyk4E6mkyRz2kaQOGf6S1CHDX5I6ZPhLUocMf0nqkHf7rBM/9H2++fPVvDH814kf+j7f/Plq3jjsI0kdMvwlqUMO+0jSjM1iihbDX5JmbBZTtDjsI0kdmkn4J3ldknuSfCnJjlnUIEk9m3r4JzkBeC/wY8C5wFuSnDvtOiSpZ7O48n8l8KWq+nJVPQJ8FNg6gzokqVuzCP/nA18bWz/Q2iRJU5Kqmu4Jk0uBH62qt7X1nwBeWVVvP2K/7cD2tvpi4J6jPOXpwINHeezxyj73wT734Vj6/PeqatOkDbO41fMA8IKx9bOArx+5U1XtBHYe68mS7KuqxWN9nuOJfe6Dfe7DUH2exbDP7cA5SV6U5CTgcuCGGdQhSd2a+pV/VT2a5F8DNwEnAB+oqi9Muw5J6tlM3uFbVZ8APjGl0x3z0NFxyD73wT73YZA+T/0FX0nS7Dm9gyR1aG7Df56mkEjygiS3JLkryReSvKO1n5bk5iT3tseNY8dc2fp+T5IfHWt/RZI/b9t+I0lm0ae1SHJCks8lubGtz3V/AZKcmuT6JHe3n/cF89zvJD/X/k3fmeQjSU6ex/4m+UCSQ0nuHGtbt34meWaSa1v7bUk2r1pUVc3dF6MXkv8COBs4CfjfwLmzrusY+rMAvLwtPwf4P4ymxvhPwI7WvgP4j2353NbnZwIvat+LE9q2zwAXAAH+CPixWfdvhX6/C/gwcGNbn+v+tnp3A29ryycBp85rvxm9ufMrwLPa+nXAT81jf4EfAl4O3DnWtm79BH4W+K22fDlw7ao1zfqbMtA3+gLgprH1K4ErZ13XOvZvD3Axoze+LbS2BeCeSf1ldGfVBW2fu8fa3wL811n3Z5k+ngXsBV7D4+E/t/1t9T23hWGOaJ/LfvP4u/1PY3TzyY3Aa+e4v5uPCP916+fhfdryBkZvCstK9czrsM/cTiHR/pw7D7gNOLOqDgK0xzPabsv1//lt+cj2p6OrgV8E/nasbZ77C6O/VJeAD7bhrl1Jns2c9ruq/i/wHuB+4CDwV1X1Sea0vxOsZz+/d0xVPQr8FfB3Vjr5vIb/pPG+4/62piSnAL8PvLOqvrXSrhPaaoX2p5UkbwQOVdX+tR4yoe246e+YDYyGBt5XVecB32U0HLCc47rfbYx7K6Ohjb8LPDvJW1c6ZELbcdPfp+Bo+vmUvwfzGv5rmkLieJLkREbBf01Vfaw1P5BkoW1fAA619uX6f6AtH9n+dHMh8KYk9zGa9fU1ST7E/Pb3sAPAgaq6ra1fz+iXwbz2+0eAr1TVUlX9DfAx4NXMb3+PtJ79/N4xSTYAzwO+udLJ5zX852oKifaK/vuBu6rq18Y23QBsa8vbGL0WcLj98nYHwIuAc4DPtD8tv53kVe05f3LsmKeNqrqyqs6qqs2Mfnafqqq3Mqf9Payq/hL4WpIXt6YtwBeZ337fD7wqyfe1OrcAdzG//T3SevZz/LnezOj/zMp//cz6RZABX1x5PaO7Yv4CePes6znGvvwgoz/hPg/c0b5ez2hMby9wb3s8beyYd7e+38PYnQ/AInBn2/ZfWOVFoVl/ARfx+Au+PfT3ZcC+9rP+A2DjPPcb+PfA3a3W32V0h8vc9Rf4CKPXNf6G0VX6FevZT+Bk4PeALzG6I+js1WryHb6S1KF5HfaRJK3A8JekDhn+ktQhw1+SOmT4S1KHDH9J6pDhL0kdMvzVlSSb2zz5v93mkf9kkmcts+/5ST6f5M+S/Orhudgz+pyBX01ye9v+L1r7RUluzePz8V8zNt/6K5J8Osn+JDeNva3/3yT5Ynuej07r+yAZ/urROcB7q+olwMPAP15mvw8CP1NVFwCPjbVfwWgGyvOB84Gfbm/Dh9GMq+9kNCf72cCFbV6m3wTeXFWvAD4A/Ie2/w7gvKr6AeBn1qd70upm8gHu0ox9paruaMv7Gc2z/gRJTgWeU1X/qzV9GHhjW34t8ANJ3tzWn8foF8ojjOZgOdCe44723A8DLwVubn8InMDorf4wmsbhmiR/wGg6B2kqDH/16K/Hlh8DJg37rPQxgAHeXlU3PaExuWjCc29o+3+h/QVxpDcw+pSnNwH/LslLajQfuzQoh32kCarqIdoMiq3p8rHNNwH/sg3nkOQftA9dWc49wKYkF7T9T0zykiTPAF5QVbcw+uCaU4FT1rkr0kRe+UvLuwL47STfBW5l9OlIALsYDed8tr2guwRcstyTVNUjbYjoN5I8j9H/u6sZzTr7odYW4D9X1cNDdEQ6krN6SstIckpVfact72D0eavvmHFZ0rrwyl9a3huSXMno/8lXgZ+abTnS+vHKX91L8l5GHx057ter6oOzqEeaBsNfkjrk3T6S1CHDX5I6ZPhLUocMf0nqkOEvSR36/2tPZXcJIPSYAAAAAElFTkSuQmCC\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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3j3imLmmt9eWZUmWgOCIIjs6gkq2px63MlW7ud8hkFWhnSorm5veyn2RT9sT2a5xQzo0dTUyvr6TrwHFqKspiDt2V69+lJCQj8iU5PYOgta6S1TfPYWvPUcImstJoqinnt3cu4OAJ/zPUqjJQHOH3QBME7OR2c9NTNrjlf8gmZbNf6bezKXt8+9VUlPHFy1pGHGIfPaR+79F+rrpw6qh8SV9/8g06r2jjvCnjU9bRwKBhzXPdI+p1en0lZ0/2f0KlieoUR7h15GQQ4uozIZnc0UEwkygTN3ErGiebxG9uxfunSzZlj2+/Hy29OKYI4Mwh9dEIn5KQtdM3bEhZR24n1HMbXRkEiGyiIbyeabs1+81Xc1Mque1MPeGwYffhPvYfO0XfwCAttZVMr/dmtpxsZu5VKomgkO2qJNp+qQ6pv6Sl1nIFYkzqOgp6vaoyCAiZLs1z5dh1ywQQ9AcinvgBNCQSsx1HSSV3OGzY8M5+duw/McLs8E83Xszss6rZe9R95W2llLJJJRHFbhUYJJOfG74Xu7IvmFHPkjlTaa6pGDUpipqRUq2Ug7CpLhlqJgoImS4hc7n0dMME4Ja5yWsSzUJfevhlvnhZC40TzsiZSu6dvX1s7Tk6yuzwt7/cwhOv7cmZmSzTVBKpTC75ZvJzcr6CXdkvaa2lbVIVY8aEWDx7Cr+9cwGrb55D5xVtPPrirlgG02QrZT831TlBVwYBIdMZcz7NtMHdDJNeYjWArly/g84r2li1vsuR3PuP9RM29jbm6N9em8nS7SPxq8D9x/qpKCthYCgc25kbnQTkk8nP6erIyQo4FBLOnlzF9PpKZjWO5xNn1zlaKQf9fGtVBgEh0yVk0JeeiQT9gYhiN4DOmTaRtZ2XjpLbylzSML6ckmFbs5WNOf6+XirvTPpINErq7X3HufVnmywH0HyaiKSjuJyamxKvGxwM88aej9h7tJ/GCeOY3Th+1HnNboURe4EjM5GI/KnFe19yX5ziJdMlZNCXnlb4FXGSDnbmrJa6ylFy25lLmmsquKBpAisWtY9on698+hyeeLVnxH29VN6Z9pFU5qV8MfkB9Pad4rbL27hj4QzuWDiDxgnlruaAGhwM89Tre1i65kVu/7+vsnTNRp56fQ+Dg+HUXw4ITlcG3xSR64G/A6qAB4BTwM+9EqzYyHTG7MZMO0hOwKCQjjnLbtBct3wBC89tYMakKuY213ByYJBpNRXsPNzHkZMDQG6Ud7SPnHvnAnYf7qOibAwN48em/F6qmb/XJj+3+mU4bPjwo/7Y7t+o0/cXm3e7pri27T3Kjzfs4LbL25BhEX+8YQftk6u4aFqNK7/hNU6VwSeBvwW2DL/+pjFG0027TKZLyGyWnvmaZsJrBZaOkk05aNZXjchn0zapyvP0DFa8s/94Wu2cyrzkpckv3X6ZrD/s7O0btVFs1YYdrLmlI6XictrPevtOjUpHvXxhO4f7TqVdbr8mZk6VQQ2RE8reI3ImQYuIiMnHLHfKCPLJCRgll+G0TpRsujZ5P+zGmbSzk5m/V2VJR95U/cFOWZeWSFKfD+C4n40vLxuRjrqmooz+wSFOD0US4TkZ1P2emDkNLX0R+J0xZjFwCXAW8EfPpFJyRhDy6adL0HZy5oPfxmk7J+b1/8zMhqQ7q52Ea3opL2Tu22gYXx4rg5XPZ/dh5/1sYDA8IrfRLfNbWPNcN52PvuI45Nbvfu10ZfCnxpjdAMaYj4HlInJF9EMRmW2M2eaFgIq3BD0ayWrGFrQolnyIkLJr50lVow+nsZqZWtWrlzPZdPpltr4Nu0H457fOGzHAR1NZHzxxalT7TplwRt4lc5tGHVrjZLXtd792tDKIKoKE956Le/moaxIpjnFjVhbkWa3djG1ydfCiWIIcIRUOG4yB799wESsWRSJpyktDrFjUzvu9Z/pNujNTL2ey6fTLxJl/44Ryli+awcmBIUcnlNkNwicHBikvDcVm+g++0M3qDV186aGXeWbbPnYeOvPsRXcml5eGEJtDa1Kttv2OznJrn0Fwen6R4NasLFez2kwcY3aDzTMrFuTFxjW3yMapaNVP7r5qFsc+Ps0jGyM7Z3975wLOnmyfl8duZurlTDadfmmVdXTtpt0MhWHLBx9xSUstl7XV2fo27FYhzbWR+76975jlTD9xA2LUpHbwxCkeeL7b0arGrhx+9Gu3lIE6knOMm45frx2amSouu8Fm37F+TxVYLiM6Uv1Wtkrfqp/c8/R2bru8LZZnaffhPs6eXJW2ydBrE2M6m7+i/eHg8VP8z8dfHxXZc+/1F3L1hWelVCbxdTy9PpJUMLGMYL2LPHqyXaaDut/mRt2BnKf4bV9Mh0wVV7LBxisFlsuIDie/la3ST5aFEyL1WVEWGQbSHcT8nsnGE5911Oqg+bse38oFUyekTL9hNQi31I5WCMl2kac7qFtNCPx4ht1SBgN2H4jIYmAlUAI8YIz5rsU1nwJ+BJQCh4wxn3RJroIl6I7feDJVXH4MNomDb01FGW/vO0Z5aYjWukpXZ2pOBvpslb5dPzHmzG7o6Aa0dAcxv2eyVjSML7c9c8BJnVkFy5eEYMWi9hGZZ1csaueRjbti1yQ+e/GTlWSrP7/DSeNJqgxE5LNAtTHm3xLe/3PggDHm3wGMMfNtvl8C3Ad8GugBNonIr40x2+OumQj8BFhsjNktIpOzKE/REKRZWSoyVVx+DDbxg2/UcRhvbnDzQXUy0DuJAkpGc00F37r2fL7x1JsjfAbH+0/TeUUbjRPKaZpYEbs+3RWXH3sm4gfXxgnlDIXhwPHIQNtcU2F75oBdnaUakPce7eeRjbtiu4vLx4SoKC1xtIs81b3tJgR1t85jUvXYQJmJ/hdwtcX764EngX9P8f15QJcxphtARNYC1wDb4665GXgiLnT1gAO5i54gzsrsyEZx5XqwiR987UIEzx12uLr5W1ESlaRV3UWjgFIdkhMOG7btPcq+o/1874aLCIfDvHeoj9UbumL+gvLSELPPsjafBJH4wdXqiMoffP5iZjVWjzpofsWidkpsYidTrdAaxpdz5OQA9/2hK/adlrpx/O8bLqLrwHE6Wmr5RFudZVukurfdhOD5rkM88Hx3TlcJqZRBhTHmYOKbxph9IuJkCjoV+CDudQ+RnczxnAOUish/ANXASmPMI4k3EpFOoBOgubnZwU8XPkHOgBhPviouuxDBqMPVzd+yU5KhkDCrsZrOK9oIm4gZIxoFtC6J38AuiuiXm3vSOqAnaMQPrkvmNo06K+Irj23hx8vmsHpDV2wmH62zOc0TR6QFiZLJPoWlHc3847q32Hu0n5a6cay6aQ4nB4aoKBvDwNAQdZVjHe2JSWbGyyYoJBNSKYNyERljjBmMf1NESoFxDu5v9bQnWuXGAH8CLBq+50YRedEY8+6ILxmzBlgD0NHRodFLeUY+Kq49H33MAxYPKiKxs4/d+q1kSnLv0X5Wre8a9f1kg7hdFFE0HDK+PInmOj/z46T67fjB1U5ZnxoMj5rJZxMVFd9O7+4/zht7jvHoi7uGU1WXs7SjmaVrXowpimgSvLsWz+Tchuqk97ZSNNGT06LlCcqmsyeAn8avAob/vn/4s1T0ANPiXjcBH1pc84wxps8Ycwh4DrjIwb0VC7xKD1BMRBXXtJpx3H3VrBEbn5YvbOcffv1mxhurEtsHSLlZLZPNSHYz0ubaithGquWLZvD9Gy7CGGL9xM/Ty5z8dmJdWNXLB4dPjkob7iQqKtn10T5xTkM1D77QHVtdWZkSV23YwVUXTuUrj21hTAnce/2FtveOKpp1yxfw8F90xE5OizfjBWXT2TeAbwG7RGQXkZn+NOBB4G4H998EtIvIdGAPcBMRH0E8vwJWi8gYoIyIGemHjkugxAhSZEIh0Fxbybv7T4wwN0Qf1PjZmtOZdKbtk4nPJbpLO3FGek5DFb9bvoDXPviIrz/5Bv2nw7TUjeOeay6gtESoKBvDvc+85cr+lXSxs69P7ZzPyYGhmIM4WhePv9IzKspn+cIzUT4/v3UeBuNqVFRiW9hFLolEItFe2fURK9e/y22Xt1ESYpR/Ib7vNNdWMmQMa57rBnKfDUCcJB4VkXHAjOGXXcP5iZz9gMiVRMJGS4CHjDHfFpHbAYwx9w9f81XgViBMJPz0R8nu2dHRYTZv3uxUhKKh++AJrlz1/KgBIJltudhI1wTy3oET/NmP7es0nQE+m/aJyu3U57Lz0Al+9+a+UeGQnzt/CmFDTA6riKmomSLet7C281Lmt9UnlTFbNr53iGU/fWnU+8sXzRi103f3kZMcON7PlPHlHPv4NP/v7QMMheGJVyM+ES/7fXxbjCsdw9I1G0e1aXTwX/Pc6J3IqfrOrMZq9h3zxrcmIq8YYzqsPktqJhKRJSKyBPgc0E5EIXSISLXTHzfGrDPGnGOMOdsY8+3h9+6PKoLh198zxswyxpyfShEo9uRjBtJckokJZHp9chNCOvl5smmfdHMfxYdD3rFwBrdd3sYjG3ex71j/CDnszBxL5jbF7lVeGmJcaYnnpkc7c9jQcJVF63b3kZOxumitr+L8qRM5b8r4mPnG6xl1fFtcMHXCqP5x91WzeKn7IOdMrk7a3nZ9J2zwJcdVKjORVVhpLXChiNxmjNnggUxKhuTTRrR0ccOpmclu3lQmhHQ2heWyfazCIeN/KyqHnRM2GoZZXhriW9eez/K1r7Gr92NPTY+pnKlR2RLr1s9otVBI+MzMBtbc0sHmXYcZCsOa597jK58+l5lTqmmpG8eNfzKNSVVjqRg7hr0fnWTKcOrsoGURSKoMjDG3Wr0vIi3AY4wOE1V8JFcb0XIdbeKWLyTThy9ZJFQ6A3wuNwqm+q3oZ1F5E+VfdN5kPnF2HeNKS2KKALz1ISQO6tHfjjdX2dWtn9Fqu4+cpPPRzSPq8K7Ht/L7v1nAP1w9m9c++IgPPvqY37y+h5suaabr4AmaaysDN3nLKB2FMWbXcHipEiByMUPyw0m9+3Afb+87xpcXtAHw+Cs9GQ1IXjx86QzwuZzBpvqt6GeH+07RPrkqdixkVP4Lpk4kFBI2vncopgii9J8Ox45ztNoF7PRUL6sJRWIah7sWzwz8LnurSUZNRRmbd55x0kdXOWs37eaai6fSVu/9GdLp4siBPOpLIucBDxtjLnNfpNSoA9k/cu2kDocNT23ZM+qhevTFXfxw6UVpOTW9UmTpOneDRjL5rdq7pW4cX/n0udz1+FbbXcDJ6jSddsiHurWqo+WLZlg6j6ORaZfPqGNeax3vH+pj1+E+KseOoaF6LM213pYvmQM5VW6i3zB6k1gt0Ah8wR3xlHwi13bOnb19MUUQ/a1VG3bQeUVb2jN6r2bm+bKhzo5k8rfWVbL65jls7TlK2ECJwCXTa/nyzzcn3QWcbNX2/iFr3000zUdQsnjGY7eSiT84aMeB4zy2uYcjJwdsncdRX0xpKMRvtn44akXWXOvfqieVmej7Ca8NcJiIQvgCsNELoRR38MK2n2s7p53yOaehOqPldL4P3OngVvsPDJrYLLe8NER73ECX7FQvuzredbjPNs3H9PrKwO2VsVvJfGZmA79/a/+I979z3QXMbZ7IUNjaFzNzyngOHu9nY3cvq//Q5ct+DjuShpYaY/4z+g84ClwFPE0kgd1bOZBPyRCvdpLm+phMu3DDmVPGB85cECTcan+rCKwdB46n3AWcbHJQWTbG8jsVZWN8ORQ+1a59O5m27T066v2vP/kGYWMdkvzt6y6g90Q///yf3fQPhgMXBp5qn8E5IvJNEXkLWE0k6ZwYY/6rMWZ1TiRUMsKrhyp++7zVebJuY6d8ptcHy4kYNNxqf6uV2WObe/jOdRdQXhqK7QJOZ3LQMH7sqO+sWNTOlAljOXj8FF9eENkb0TihPCa7V4OkE6Vptzrde9TeZGr1nDTXjuMbv9o+ItVEPH6HgacyE70NPA9cbYzpAhCRv/FcKiVrvD6fNlemlnzKeBok3Gp/K7PgkZMDzG2eyLrhNpkyvpzPzJrCwRPO2qe5tpL2hqpYJtaQwKyzqtn24XHLPQZHTg6MGCTdNH862XtiZxptnDAuZYK7xOckev3jr/SwfGH7qLMy/IyUSqUMrieST+gPIvIMsBbrTKRKwAhaDLNT7B70YrHzJ5LpwOdW+9uFP0ajXuLbxGla71BIWHhuA231VTEFbwwj0n7EBwqcN2V8bJB0OyLMidK0q4PZjeMzPiZ079F+frF5N2tu6aC0RHKeHdYKp7mJKoFrgWXAQuDnwJPGmN97Kp0NGlqamnxMWpePMntJNvXhZl3mIrzTLi/Rw3/RwSfPmZw01DWb0Gan97Org3TrJtX1Xm/oTBZamvY+AxGpBW4ElhpjFrogX9oUgzJwo1PkQ4x2PJpobyTZ1kc+tb/TstopjWSJ9PLlDOJcyJLxPgMrjDGHgf8z/E/xALc6Rb6ZV4KWq8Vvsq2PfGp/p7tx0zV/pXqWguSTyiR3lpukOtxG8QE/wuuCQCaHuBQyxVQfTqPU0g1tTudZyiAZg6v4nXU4o9xE+YifR/mlS7HOkJtrKvjWtefzjafejM3ivnXt+TTXVPgtmiPSOeTGyXVBy13jNU5WMunO5FM9S0EyE/kd9FEUyiBIDe4EvzuFX+w+cpIfb9gx4mSxH2/YwdzmmsArQad9LJ2+GCQTRpAmU+mYv1I9S36bZuLxW/lnlKjOb9J1IOebYzLIysvLQSET52BQcNrHdh46wROv7SG6p+nxVyK5bILaFyHY/TEVqWR3s8/lQ9CHqw7kfCTfzC5BmhHG4/WgkM8rIrs+tv9Yf+zzxgnlvLr7oxF5fqIbq4LaFyFYs+d0SfUsudXnCiHow3MHsogsFpF3RKRLRL5m8fmnROSoiGwZ/vdNt2XIR0dcuscc5gKvHdu5znvkJnZ97PSQiaU6eOK1PZYZWG/saAp0X3TDsZkq/49b37H6HmD7LLnV5woh6MPTlYGIlAD3AZ8GeoBNIvJrY8z2hEufN8Zc5ZUcftviCgWvV1jproiCZMe26mP3Xn8hd//qzOAfNtYZPjPNwJorsp09p5o1W7UjkNFMO90ZulWfa66pSLtf5Zv1wQqvzUTzgC5jTDeAiKwFrgESlYGnBNXskm/kwozjdJnsxQ7bbJSKVR/r7Ts16pQwu7TGQe2L4bAhJPCd6y4YccBQOpOpZGam1jrrlNXnNlRnZJrK9Jzr+NPVMulX+WzijOK1mWgqkUynUXqG30vkMhF5XUR+JyKzvRAkiGaXfCNIZhy3luVWWSt/s/VDNu3sTcs0AaP7WF3l2BGmI7sMn0HNwBqtm8Urn+d7z75D5xVtrL55Dr+9M3mm2kQzTW/fKdtZs1072p15kMo0la1JK9N+FaRnI1O8XhlY9ZbEp+tVoMUYc0JErgSeAtpH3UikE+gEaG5udlnM3BIk80Y6BGmF5day3Orhv+vxrdx2eRsPvtCdlYM80XR05OQA7Q1V/PbOBY4zfPpJfN3sPdrPqvVdsQgpu3w6jRPK2b73+ChzWUvduBGrpOis2a4dK8eOyWimne0MPZ1+lfgcf2ZmQyyTa9Db1gqvlUEPMC3udRPwYfwFxphjcX+vE5GfiEi9MeZQwnVrgDUQCS31TmRvyecwPQhOigO3luV2D3/0BK9somaSKU+nGT79xMnAmNifE8/+jSrXNbd00PnoZkszk1U7NlSPzcjPl61/0Gm/SvYc+/1sZIrXymAT0C4i04E9RNJh3xx/gYhMAfYbY4yIzCNiuur1WC7fyOcwvSDhVlCA3cMf3X6TrRMwKMozE5wMjIn92c5JXloilrPmZCmym2srU65CrVbZ2axenfarQnyOPVUGxphBEbkDeBYoAR4yxmwTkduHP78fuAH4axEZBD4GbjL5uBPOIYUQdRAE3DJZWT380dh/yD8noJs4GRit+rPlTH98uaVSTNWOyRRpqtm526u5eArxOfZ805kxZh2wLuG9++P+Xk3kSM2ioBCiDoKCG7Pu+Id//7F+Tg8Z7v7VG+w92p+XTkA3cTIwJvbnqJN85XrnJ3hl2o7ZzM6T+e2cyFOIz3FRpKMIEvnuM8g30nXW59MZAG6RTUCDVX9effMcptdVeeYkj8r77v7jvPnhMR5/pSd2rjCkTiXhxjOYr8+xq4fbBIF8VgZQnAOOH+TLA+tndFl8HdVUlHFjRxPnTK5mZuN4ptc7kyOX/dmqTaNmvehqLlWeJ7dyleXjc1z0uYmCRj47FfMJOzPC1M75nBwYCkRYr98KK1pHNRVl3DK/ZdQB7U7kSOzP0X0GXig3qzZdNZzpNhoKnMqs55a9v9CeYz3cRilY7B769W8fiG0we2bbvrQ2lrmN3zltonW0ZG5TTBFkI4fVJj4369iuTS+cOt72QJxE8jFXWS5QZaAULHYP/dDwWBKEZGJ+n24VraPovops5fBaudm1aXtDtePMAoWwW9gLVBkoBYvVQ798YTtPvNoTuyaXA68Vfs9So3VUIrgih9fKzY2B3OkRm3aEw4adh07wUncvG97ez3sH0ktbElTUZ6AULImhkeNKS1i+9rURkSd+mwf8zqgbraNZjdW01FVmnIwuitchl27tL8nU3h8OG57vOsDJU2He2neMsIH/9Ztt3LV4ZuACE9JFo4mUoiGXztp0IoSCEpXihhx+O8SdkE301s5DJ/jje73c8/T2EdFMv9i8m4f/Yl7gnckaWqoEBr+T9OVi4M2HATFTnLRfUJSbFdm2zUvdvXzp4ZdHrXxuu7yNBe11jo7K9PMZ0NBSJRAEYZDMRThgIeatAeftF+SQy2zbpm9g0NInUhLCkSksCM+AHepAVnKG32GUucLvCCGvKIT2y7ZtWmorLR3tHS21jvwrQa5DVQZKzijUQTIRvyOEvKIQ2i/btplePzqa6d7rL+QTbXWOZvZBrkM1Eyk5oxCTe1nhd4RQPG7apwuh/bJtm2yjmYJch+pAVlwj1cATZHup29g5UXPpPHS7vgul/fx0cPtdhxpNpHiO004e5EgTr8n1QJAqIVsmiqmY288t/KxDjSZSPMdplEauIk38DmG1ItdRRsns0611lRkppiBHCuULQa1DdSArrhAkx5jXydIyxYs6imYI3fjeIboPjkyLkMxZGuSoFsWaZG3tBqoMFFcIUgRNUAc6t+soldJLlscnSMo7iteDXT6TiwmOKgPFFYKUCTKIAx24X0eplF6yhGxBUt4Q3NWcE3KhxHIxwfHcZyAii4GVQAnwgDHmuzbXXQK8CCw1xvyb13Ip7uJWAjE3CGr4ntt15OSQFjv7dJDCXyF/d23nKijArQN5kuGpMhCREuA+4NNAD7BJRH5tjNlucd29wLNeyqN4S1AcY6kGOq+cy07um0kd2d03G6UXJOUN9oPd/mPuDXZekCsllosJjtcrg3lAlzGmG0BE1gLXANsTrrsTeBy4xGN5lCwIYoSOFckGOq9mcm7eN76eGyeUs33vccv7urGBKgjKG6CibIzlYFdRVuL5b2fTr3MxY4fcrOS8VgZTgQ/iXvcAl8ZfICJTgeuAhSRRBiLSCXQCNDc3uy6ocgarhwPIqw1HdgOdVzM5t+6bqFSWL5rBmue6be8bpNl9NgwMDbF8YfuIM5iXL2zn9FA49ZezIFslniuTZC5Wcl47kK0kTfSu/Ai4yxgzlOxGxpg1xpgOY0zHpEmT3JJPScDOkff+oWBG6KSLV85lt+6bqFTCJvlxlFGlN7+t3vGxj0GkrnIsv9i8m9sub+OOhTO47fI2frF5N7WVYz393Wwds7kMnPC6rb1eGfQA0+JeNwEfJlzTAawVEYB64EoRGTTGPOWxbIoFdg/HT/58bk6Ww17j1UzOrftaKZX4+zZOKOfGjiZODgzRffBE3q4EEmmtq+SuxTNz7tDO1swTNN9LNnitDDYB7SIyHdgD3ATcHH+BMWZ69G8R+RnwtCoC/7B7OCrHWtt0/Y7QSRevbK9u3TdRqTz+Sg8rFrWzcv0OairK+OJlLaxcvyMjk0aQ8WtQdUOJh0ISa+f9xyIrNqeyB8kP53luIhG5kogpqAR4yBjzbRG5HcAYc3/CtT8jogyShpZqbiLvsMtn88yKBbaOzHwbiLzKDePVsZGrb57D9LoqDp04ZXnK1rqAh18GGTcc/5new4+kdZqoTnFMsg4KaJKyHGCnVDa+d4hlP31p1PVrOy91dNyiYk22SjxVQkC3v5cNmqhOcUyq5XpQQhGDgFdLfLtIqKBupst3sg2xzdTvkKuwVKeoMlBGEaT486DixxI/aLuGlQiZKumgKXc1EylKBvixxIfCP0/AbrXll6PVye8Wis9AVwaKkgF+LfELedUWPzjWVJRxY0cT50yuZuaUat4/3Mcd//JaToMXnA7WmUZCBS0sVZWBomRA0Jb4hUB0j0tNRRm3zG8ZsRt5xaJ2airK2Hu0P2dJ7NLZVZ6pkg6SctcU1oqSAUFK2V0oRFdbS+Y2xRQBRAbhlet3sGRuU+xaq93dbqeSDmoqdK/QlYEPBGmjiZIZQVviFwLR1ZaIdQoOiavaxFWYF/b3Ylv96cogx+TzIR7KSAolL1BQiK62SgTLg3ei1Wu1CvPi8JdiW/3pyiDH5OshHoriNdHV1qzGalrqKvn6k2+MmOXPaqzmE2fXWa7CvHDoF9vqT5VBjgnaRhNF8Zp0zKKhkNBaX0VzbSUXT5s4ahBura+yvHdIxBOTTpAcvF6jyiDHFJsdUiluMrXlOxmEE0NRown9dENeZuimsxzjx0YTRfELLzfnJd47mt57zrSJtNRVFrRJJ1N001mAKDY7pFL4JDMDeWkWTbz33qP9rFrfxdrOS4vCrOM2qgx8oJjskEphk2ql66VZVE2u7qKhpYqiZEyqkE4vwzOLLfTTa3RloChKxqQyA3lpFvXT5FqIG0dVGShKnhDEAciJqcZLs6gfJtdCDQJRM5Gi5AFB3blejKYaL3Y7BwFdGShKHhDUnevFGB1XqBtHPV8ZiMhiEXlHRLpE5GsWn18jIltFZIuIbBaRy72WSVHyjSBn0Cy2HE1R01g8hRDF5KkyEJES4D7gc8AsYJmIzEq4bD1wkTHmYuAvgQe8lElR8pFCHYDykUI1jXltJpoHdBljugFEZC1wDbA9eoEx5kTc9ZVA/m2JVhSPKfTzj4PoHLejUE1jXiuDqcAHca97gEsTLxKR64B/BCYDf2Z1IxHpBDoBmpubXRdUUYJMoQ5AkJ/ROYW4cdRrn4FVS46a+RtjnjTGnAdcC9xjdSNjzBpjTIcxpmPSpEnuSqkoeUCh2uYLNTon3/BaGfQA0+JeNwEf2l1sjHkOOFtE6j2WS1GUgBBk53gx4bUy2AS0i8h0ESkDbgJ+HX+BiMwQiRxoJyJzgTKg12O5FEUJCOocDwaeKgNjzCBwB/As8BbwmDFmm4jcLiK3D192PfCmiGwhEnm01ORjXu0ixe1DyPOBYiyzl9hF5zTXVGg95xA9z0DJmHx0/GVLMZY5F0SjiaLO8eaaCn7/1n6tZ5dJdp6BpqNQMqYYHX/FWOZckOgc333kpNZzjlFloGRMMTr+irHMfqD1nHtUGSgZU4yOvyCVuZB9F0Gq52JBlYGSMYW6LT8ZQSlzULOYukVQ6rmYUAeykhWJjr9C2RWbjCCU2cuD5oNCEOrZb9xO05HMgawprJWsKMRt+akIQpkLNY1yPEGoZz/JdeSamokUJQ9Rm3rhk+vINVUGSqApZCdpNqhNPX/ItA/nOqJKzURKYNENXvYUchbTQiKbPuzkfGk30ZWBElh0g1dyCjWLaSGRTR/O9epPVwZKYCkGJ6lS2GTTh3O9+lNloASWXC+TFcVtsu3DuYyoUjOREljUSarkO/nUh3XTmRJodOORku8EqQ/rpjMlbyn2jUdK/pMvfVjNRIqiKIoqA0VRFEWVgaIoioIqA0VRFAVVBoqiKAp5GloqIgeBXS7cqh445MJ9/KZQygGFU5ZCKQcUTlkKpRyQeVlajDGTrD7IS2XgFiKy2S7mNp8olHJA4ZSlUMoBhVOWQikHeFMWNRMpiqIoqgwURVEUVQZr/BbAJQqlHFA4ZSmUckDhlKVQygEelKWofQaKoihKhGJfGSiKoiioMlAURVEoAmUgIotF5B0R6RKRryW57hIRGRKRG3IpXzqkKouIfEpEjorIluF/3/RDzlQ4aZPhsmwRkW0i8p+5ltEpDtrkq3Ht8eZwH6v1Q9ZkOCjHBBH5jYi8Ptwmt/ohpxMclKVGRJ4Uka0i8rKInO+HnKkQkYdE5ICIvGnzuYjIquFybhWRuVn9oDGmYP8BJcB7QBtQBrwOzLK5bgOwDrjBb7kzLQvwKeBpv2V1oRwTge1A8/DryX7LnU3/irv+amCD33Jn2CZfB+4d/nsScBgo81v2DMvyPeDvh/8+D1jvt9w2ZbkCmAu8afP5lcDvAAHmAy9l83uFvjKYB3QZY7qNMQPAWuAai+vuBB4HDuRSuDRxWpag46QcNwNPGGN2Axhjgtou6bbJMuBfcyJZejgphwGqRUSAKiLKYDC3YjrCSVlmAesBjDFvA60i0pBbMVNjjHmOSD3bcQ3wiInwIjBRRBoz/b1CVwZTgQ/iXvcMvxdDRKYC1wH351CuTEhZlmEuG17K/05EZudGtLRwUo5zgBoR+Q8ReUVEvpgz6dLDaZsgIhXAYiKTjqDhpByrgZnAh8AbwApjTJjg4aQsrwNLAERkHtACNOVEOndx3P+cUOgnnVmdLZcYS/sj4C5jzFBk0hNYnJTlVSK5R06IyJXAU0C714KliZNyjAH+BFgEjAM2isiLxph3vRYuTZyUJcrVwB+NMclmen7hpByfBbYAC4GzgX8XkeeNMcc8li1dnJTlu8BKEdlCRLG9RjBXOalIp/+lpNCVQQ8wLe51E5GZTTwdwNphRVAPXCkig8aYp3IioXNSliX+wTTGrBORn4hIvTEmSMm5nLRJD3DIGNMH9InIc8BFQNCUgZOyRLmJYJqIwFk5bgW+ayLG6i4ReZ+Ivf3l3IjoGKfPya0QccIC7w//yzfS6X+p8dtJ4rEDZgzQDUznjDNpdpLrf0ZwHcgpywJM4cxGwnnA7ujroPxzWI6ZRGy6Y4AK4E3gfL9lz7R/AROI2H4r/ZY5izb5Z+Afhv9uAPYA9X7LnmFZJjLs/Ab+iojd3XfZbcrTir0D+c8Y6UB+OZvfKuiVgTFmUETuAJ4lEmXwkDFmm4jcPvx50P0EMRyW5Qbgr0VkEPgYuMkM95qg4KQcxpi3ROQZYCsQBh4wxliG1/lJGv3rOuD3JrLSCRwOy3EP8DMReYPI4HOXCdaKE3BclpnAIyIyRCRq7TbfBE6CiPwrkQjBehHpAf4eKIVYOdYRiSjqAk4yvNrJ+PcCNlYoiqIoPlDo0USKoiiKA1QZKIqiKKoMFEVRFFUGiqIoCqoMFEVRFFQZKIqiKKgyUBRbROQ6ETEict7w60+JyNMJ1/wsmvZcREpF5LsismM4XfXLIvI5P2RXlHRRZaAo9iwDXiCSSsIJ9wCNRHZLn08kH1G1R7IpiquoMlAUC0SkCvgvRHanplQGw1lJ/wq40xhzCsAYs98Y85jN9beJyA/jXv+ViPzAFeEVJQNUGSiKNdcCz5hIptTDDk6RmgHsNs6zeK4F/puIlA6/vhV4OCNJFcUFVBkoijXLiAzYDP+/DPv0wGnndBnOU7QBuGrYJ1FqjHkjE0EVxQ0KOlGdomSCiNQRydt/vogYIgnPDPAIUJNweS1wiEiysGYRqTbGHHf4Uw8QOU7ybXRVoPiMrgwUZTQ3EElr3GKMaTXGTCOS774WOEtEZgKISAuRcxa2GGNOAg8Cq0SkbPjzRhH5gt2PGGNeIpKP/maCe9aBUiSoMlCU0SwDnkx473EijuQvAA8Pn5L1b8CXjTFHh6/5BnAQ2C4ibxI5ae5git96jMgJaEfcEV1RMkNTWCuKjwzvW/ihMWa937IoxY2uDBTFB0Rkooi8C3ysikAJAupAVhSPEZGXgLEJb99ijDnHD3kUxQo1EymKoihqJlIURVFUGSiKoiioMlAURVFQZaAoiqIA/x8W8orpNJ8HkAAAAABJRU5ErkJggg==\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 }