{ "cells": [ { "cell_type": "code", "execution_count": 1, "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=10, max_count=10000):\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": 3, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'hicmatrix'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\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[0;32mfrom\u001b[0m \u001b[0mhicmatrix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHiCMatrix\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mhm\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;32mfrom\u001b[0m \u001b[0mhicmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMatrixFileHandler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'hicmatrix'" ] } ], "source": [ "from hicmatrix import HiCMatrix as hm\n", "from hicmatrix.lib import MatrixFileHandler" ] }, { "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": 196, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ENSG00000223972 1.00000\n", "ENSG00000227232 1.00000\n", "ENSG00000278267 1.00000\n", "ENSG00000243485 1.00000\n", "ENSG00000284332 1.00000\n", " ... \n", "ENSG00000100312 1.00000\n", "ENSG00000254499 0.99999\n", "ENSG00000213683 0.99999\n", "ENSG00000184319 1.00000\n", "ENSG00000079974 1.00000\n", "Length: 55411, dtype: float64" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jac_corr_intra.max()" ] }, { "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": 201, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"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_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" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'hm' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mexp_file_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf'/grid/gillis/data_norepl/lohia/hi_c_data_processing/software/CoCoCoNet/networks/human_prioAggNet.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mjac_exp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhiCMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mall_genes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mjac_exp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcut_intervals\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mdf_exp_corr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjac_exp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_genes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mall_genes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'hm' is not defined" ] } ], "source": [ "SRP_name='aggregates'\n", "resolution='40kbp_raw'\n", "exp_file_path=f'/grid/gillis/data_norepl/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": 13, "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": 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": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_gene_table.groupby(marker_gene_table.index)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Immune system-Pro-B cells 20\n", "Immune system-Pre-B cells 18\n", "Immune system-Naive B cells 22\n", "Immune system-Memory B cells 23\n", "Immune system-Plasma B cells 23\n", " ..\n", "Teeth-Odontoblasts 12\n", "Teeth-Endothelial cells 9\n", "Teeth-Immune cells 12\n", "Teeth-Glial cells 9\n", "Teeth-Epithelial cells 11\n", "Length: 229, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_gene_table.sum(axis=1)" ] }, { "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": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "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(axis=1))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1936, 1936)\n", "(1936, 229)\n", "0.9921302645349886\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": 14, "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_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": 31, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'df_exp_corr' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_2d_exp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgo_chrom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_egad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarker_gene_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_exp_corr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_2d_jac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'AUC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'DEGREE_NULL_AUC'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'df_exp_corr' is not defined" ] } ], "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_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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6015668744373308" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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index0
0Immune system-Pro-B cells20
1Immune system-Pre-B cells18
2Immune system-Naive B cells22
3Immune system-Memory B cells23
4Immune system-Plasma B cells23
.........
224Teeth-Odontoblasts12
225Teeth-Endothelial cells9
226Teeth-Immune cells12
227Teeth-Glial cells9
228Teeth-Epithelial cells11
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229 rows × 2 columns

\n", "
" ], "text/plain": [ " index 0\n", "0 Immune system-Pro-B cells 20\n", "1 Immune system-Pre-B cells 18\n", "2 Immune system-Naive B cells 22\n", "3 Immune system-Memory B cells 23\n", "4 Immune system-Plasma B cells 23\n", ".. ... ..\n", "224 Teeth-Odontoblasts 12\n", "225 Teeth-Endothelial cells 9\n", "226 Teeth-Immune cells 12\n", "227 Teeth-Glial cells 9\n", "228 Teeth-Epithelial cells 11\n", "\n", "[229 rows x 2 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marker_gene_table.sum(axis=1).reset_index()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "z = df_2d_jac.merge(marker_gene_table.sum(axis=1).reset_index(), left_on=df_2d_jac.index, right_on='index')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Immune system-Naive B cells0.6681001160.9949050.5023451.481502e-02
Teeth-Immune cells0.6695311050.5390330.4611051.544942e-02
Immune system-Memory CD8+ T cells0.6698251300.5625160.7244141.941642e-02
Immune system-CD8+ NKT-like cells0.6718671066.9293530.4584981.184006e-02
Immune system-Memory B cells0.6738651170.4484630.5151789.618105e-03
Immune system-Plasma B cells0.6738651170.4484630.5151789.618105e-03
Immune system-Effector CD8+ T cells0.6746141296.3542020.7246573.649295e-02
Immune system-Plasmacytoid Dendritic cells0.6843091084.4405900.5025851.043411e-03
Testis-Immune cells0.6857921074.5233480.4937901.165755e-02
Eye-Tachykinin GABAergic amacrine cells0.6935201057.9683540.3824502.052199e-03
Eye-Cholinergic GABAergic amacrine cells0.6935201057.9683540.3824502.052199e-03
Pancreas-Gamma (PP) cells0.6953431087.2786810.4196548.274722e-04
Immune system-Pro-B cells0.6959041220.1005140.5337955.265646e-03
Gastrointestinal tract-Goblet cells0.7026151087.8902270.5001157.532487e-05
Kidney-Immune cells0.702998980.3542270.4171072.404572e-06
Eye-GABAergic amacrine cells0.7060031031.3389400.3453601.877296e-03
Eye-Glycinergic amacrine cells0.7125261014.4298920.3269237.708104e-04
Immune system-Myeloid Dendritic cells0.7134031082.1509370.4610651.084595e-03
Eye-Starburst amacrine cells0.7147361068.8271510.3886951.344498e-03
Immune system-Intermediate monocytes0.7159601254.1130570.5927746.236662e-03
Immune system--T cells0.7241601229.2996250.6036091.674816e-03
Skin-Keratinocytes0.7428641155.6401640.5143663.344016e-08
Immune system-Natural killer cells0.7438181003.9778590.3676984.725138e-03
White adipose tissue-Smooth Muscle cells0.747068887.6624580.2525707.472398e-04
White adipose tissue-Adipocytes0.7597061016.1681380.3496582.410285e-03
Immune system-Non-classical monocytes0.7759681123.1798350.4938801.690176e-04
White adipose tissue-Lymphatic Endothelial cells0.850472987.6160230.2827891.775574e-07
White adipose tissue-Endothelial cells0.856215911.8010610.2551105.879036e-06
White adipose tissue-Pericytes0.915190841.0653570.1480425.620360e-07
White adipose tissue-Endometrium0.952376802.3497320.1183303.598857e-08
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "Immune system-Naive B cells 0.668100 1160.994905 \n", "Teeth-Immune cells 0.669531 1050.539033 \n", "Immune system-Memory CD8+ T cells 0.669825 1300.562516 \n", "Immune system-CD8+ NKT-like cells 0.671867 1066.929353 \n", "Immune system-Memory B cells 0.673865 1170.448463 \n", "Immune system-Plasma B cells 0.673865 1170.448463 \n", "Immune system-Effector CD8+ T cells 0.674614 1296.354202 \n", "Immune system-Plasmacytoid Dendritic cells 0.684309 1084.440590 \n", "Testis-Immune cells 0.685792 1074.523348 \n", "Eye-Tachykinin GABAergic amacrine cells 0.693520 1057.968354 \n", "Eye-Cholinergic GABAergic amacrine cells 0.693520 1057.968354 \n", "Pancreas-Gamma (PP) cells 0.695343 1087.278681 \n", "Immune system-Pro-B cells 0.695904 1220.100514 \n", "Gastrointestinal tract-Goblet cells 0.702615 1087.890227 \n", "Kidney-Immune cells 0.702998 980.354227 \n", "Eye-GABAergic amacrine cells 0.706003 1031.338940 \n", "Eye-Glycinergic amacrine cells 0.712526 1014.429892 \n", "Immune system-Myeloid Dendritic cells 0.713403 1082.150937 \n", "Eye-Starburst amacrine cells 0.714736 1068.827151 \n", "Immune system-Intermediate monocytes 0.715960 1254.113057 \n", "Immune system--T cells 0.724160 1229.299625 \n", "Skin-Keratinocytes 0.742864 1155.640164 \n", "Immune system-Natural killer cells 0.743818 1003.977859 \n", "White adipose tissue-Smooth Muscle cells 0.747068 887.662458 \n", "White adipose tissue-Adipocytes 0.759706 1016.168138 \n", "Immune system-Non-classical monocytes 0.775968 1123.179835 \n", "White adipose tissue-Lymphatic Endothelial cells 0.850472 987.616023 \n", "White adipose tissue-Endothelial cells 0.856215 911.801061 \n", "White adipose tissue-Pericytes 0.915190 841.065357 \n", "White adipose tissue-Endometrium 0.952376 802.349732 \n", "\n", " DEGREE_NULL_AUC \\\n", "Immune system-Naive B cells 0.502345 \n", "Teeth-Immune cells 0.461105 \n", "Immune system-Memory CD8+ T cells 0.724414 \n", "Immune system-CD8+ NKT-like cells 0.458498 \n", "Immune system-Memory B cells 0.515178 \n", "Immune system-Plasma B cells 0.515178 \n", "Immune system-Effector CD8+ T cells 0.724657 \n", "Immune system-Plasmacytoid Dendritic cells 0.502585 \n", "Testis-Immune cells 0.493790 \n", "Eye-Tachykinin GABAergic amacrine cells 0.382450 \n", "Eye-Cholinergic GABAergic amacrine cells 0.382450 \n", "Pancreas-Gamma (PP) cells 0.419654 \n", "Immune system-Pro-B cells 0.533795 \n", "Gastrointestinal tract-Goblet cells 0.500115 \n", "Kidney-Immune cells 0.417107 \n", "Eye-GABAergic amacrine cells 0.345360 \n", "Eye-Glycinergic amacrine cells 0.326923 \n", "Immune system-Myeloid Dendritic cells 0.461065 \n", "Eye-Starburst amacrine cells 0.388695 \n", "Immune system-Intermediate monocytes 0.592774 \n", "Immune system--T cells 0.603609 \n", "Skin-Keratinocytes 0.514366 \n", "Immune system-Natural killer cells 0.367698 \n", "White adipose tissue-Smooth Muscle cells 0.252570 \n", "White adipose tissue-Adipocytes 0.349658 \n", "Immune system-Non-classical monocytes 0.493880 \n", "White adipose tissue-Lymphatic Endothelial cells 0.282789 \n", "White adipose tissue-Endothelial cells 0.255110 \n", "White adipose tissue-Pericytes 0.148042 \n", "White adipose tissue-Endometrium 0.118330 \n", "\n", " P_Value \n", "Immune system-Naive B cells 1.481502e-02 \n", "Teeth-Immune cells 1.544942e-02 \n", "Immune system-Memory CD8+ T cells 1.941642e-02 \n", "Immune system-CD8+ NKT-like cells 1.184006e-02 \n", "Immune system-Memory B cells 9.618105e-03 \n", "Immune system-Plasma B cells 9.618105e-03 \n", "Immune system-Effector CD8+ T cells 3.649295e-02 \n", "Immune system-Plasmacytoid Dendritic cells 1.043411e-03 \n", "Testis-Immune cells 1.165755e-02 \n", "Eye-Tachykinin GABAergic amacrine cells 2.052199e-03 \n", "Eye-Cholinergic GABAergic amacrine cells 2.052199e-03 \n", "Pancreas-Gamma (PP) cells 8.274722e-04 \n", "Immune system-Pro-B cells 5.265646e-03 \n", "Gastrointestinal tract-Goblet cells 7.532487e-05 \n", "Kidney-Immune cells 2.404572e-06 \n", "Eye-GABAergic amacrine cells 1.877296e-03 \n", "Eye-Glycinergic amacrine cells 7.708104e-04 \n", "Immune system-Myeloid Dendritic cells 1.084595e-03 \n", "Eye-Starburst amacrine cells 1.344498e-03 \n", "Immune system-Intermediate monocytes 6.236662e-03 \n", "Immune system--T cells 1.674816e-03 \n", "Skin-Keratinocytes 3.344016e-08 \n", "Immune system-Natural killer cells 4.725138e-03 \n", "White adipose tissue-Smooth Muscle cells 7.472398e-04 \n", "White adipose tissue-Adipocytes 2.410285e-03 \n", "Immune system-Non-classical monocytes 1.690176e-04 \n", "White adipose tissue-Lymphatic Endothelial cells 1.775574e-07 \n", "White adipose tissue-Endothelial cells 5.879036e-06 \n", "White adipose tissue-Pericytes 5.620360e-07 \n", "White adipose tissue-Endometrium 3.598857e-08 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values(by=['AUC']).tail(30)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2430, 2430)\n", "(2430, 324)\n", "0.9943377533912513\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":133: RuntimeWarning: invalid value encountered in true_divide\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_gene_table.T, df_jac_corr_intra)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Immune system-Neutrophils0.7535671233.0898700.5893500.000156
Placenta-IGFBP1_DKK1 positive cells0.7539501234.3481060.5859670.002217
Gastrointestinal tract-Enterochromaffin cells0.7649101222.9469820.5000830.001795
Immune system-Macrophages0.7708941200.9915140.3539400.012746
Intestine-Lymphoid cells0.7785931208.6346660.3940960.004614
Intestine-Erythroblasts0.7860541221.5150290.5832540.000067
Stomach-Myeloid cells0.8047271234.8663580.6109870.000033
Adrenal-Myeloid cells0.8239531217.8828200.5831110.001632
White adipose tissue-Mesothelial cells0.8304091190.2925870.2468000.000921
Adrenal-CSH1_CSH2 positive cells0.8834541213.1868400.4551210.000007
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "Immune system-Neutrophils 0.753567 1233.089870 \n", "Placenta-IGFBP1_DKK1 positive cells 0.753950 1234.348106 \n", "Gastrointestinal tract-Enterochromaffin cells 0.764910 1222.946982 \n", "Immune system-Macrophages 0.770894 1200.991514 \n", "Intestine-Lymphoid cells 0.778593 1208.634666 \n", "Intestine-Erythroblasts 0.786054 1221.515029 \n", "Stomach-Myeloid cells 0.804727 1234.866358 \n", "Adrenal-Myeloid cells 0.823953 1217.882820 \n", "White adipose tissue-Mesothelial cells 0.830409 1190.292587 \n", "Adrenal-CSH1_CSH2 positive cells 0.883454 1213.186840 \n", "\n", " DEGREE_NULL_AUC P_Value \n", "Immune system-Neutrophils 0.589350 0.000156 \n", "Placenta-IGFBP1_DKK1 positive cells 0.585967 0.002217 \n", "Gastrointestinal tract-Enterochromaffin cells 0.500083 0.001795 \n", "Immune system-Macrophages 0.353940 0.012746 \n", "Intestine-Lymphoid cells 0.394096 0.004614 \n", "Intestine-Erythroblasts 0.583254 0.000067 \n", "Stomach-Myeloid cells 0.610987 0.000033 \n", "Adrenal-Myeloid cells 0.583111 0.001632 \n", "White adipose tissue-Mesothelial cells 0.246800 0.000921 \n", "Adrenal-CSH1_CSH2 positive cells 0.455121 0.000007 " ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values(by=['AUC']).tail(10)" ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2430, 2430)\n", "(2430, 324)\n", "0.9943377533912513\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":133: RuntimeWarning: invalid value encountered in true_divide\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_jac, go_chrom = run_egad(marker_gene_table.T, df_jac_corr)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_jac, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
White adipose tissue-Adipocytes0.7918106.942758e+070.3534141.003347e-03
Brain-Neuroepithelial cells0.7997417.563654e+070.4231015.484859e-03
White adipose tissue-Smooth Muscle cells0.8077746.047817e+070.2569305.326387e-04
Adrenal-Stromal cells0.8255807.282575e+070.3816062.510403e-03
White adipose tissue-Lymphatic Endothelial cells0.8363796.743732e+070.2865302.986060e-07
White adipose tissue-Mesothelial cells0.8602746.058658e+070.1831652.220305e-04
Immune system-Eosinophils0.8744267.586037e+070.4062811.556932e-04
White adipose tissue-Endothelial cells0.8907396.223762e+070.2588826.014601e-07
White adipose tissue-Pericytes0.9165635.733133e+070.1524334.161103e-07
White adipose tissue-Endometrium0.9518315.453354e+070.1233805.749189e-08
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "White adipose tissue-Adipocytes 0.791810 6.942758e+07 \n", "Brain-Neuroepithelial cells 0.799741 7.563654e+07 \n", "White adipose tissue-Smooth Muscle cells 0.807774 6.047817e+07 \n", "Adrenal-Stromal cells 0.825580 7.282575e+07 \n", "White adipose tissue-Lymphatic Endothelial cells 0.836379 6.743732e+07 \n", "White adipose tissue-Mesothelial cells 0.860274 6.058658e+07 \n", "Immune system-Eosinophils 0.874426 7.586037e+07 \n", "White adipose tissue-Endothelial cells 0.890739 6.223762e+07 \n", "White adipose tissue-Pericytes 0.916563 5.733133e+07 \n", "White adipose tissue-Endometrium 0.951831 5.453354e+07 \n", "\n", " DEGREE_NULL_AUC \\\n", "White adipose tissue-Adipocytes 0.353414 \n", "Brain-Neuroepithelial cells 0.423101 \n", "White adipose tissue-Smooth Muscle cells 0.256930 \n", "Adrenal-Stromal cells 0.381606 \n", "White adipose tissue-Lymphatic Endothelial cells 0.286530 \n", "White adipose tissue-Mesothelial cells 0.183165 \n", "Immune system-Eosinophils 0.406281 \n", "White adipose tissue-Endothelial cells 0.258882 \n", "White adipose tissue-Pericytes 0.152433 \n", "White adipose tissue-Endometrium 0.123380 \n", "\n", " P_Value \n", "White adipose tissue-Adipocytes 1.003347e-03 \n", "Brain-Neuroepithelial cells 5.484859e-03 \n", "White adipose tissue-Smooth Muscle cells 5.326387e-04 \n", "Adrenal-Stromal cells 2.510403e-03 \n", "White adipose tissue-Lymphatic Endothelial cells 2.986060e-07 \n", "White adipose tissue-Mesothelial cells 2.220305e-04 \n", "Immune system-Eosinophils 1.556932e-04 \n", "White adipose tissue-Endothelial cells 6.014601e-07 \n", "White adipose tissue-Pericytes 4.161103e-07 \n", "White adipose tissue-Endometrium 5.749189e-08 " ] }, "execution_count": 184, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac.sort_values(by=['AUC']).tail(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2118, 2118)\n", "(2118, 324)\n", "0.9941666958113291\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":133: RuntimeWarning: invalid value encountered in true_divide\n", " roc = (p / n_p - (n_p + 1) / 2) / n_n\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_2d_exp, go_chrom = run_egad(marker_gene_table.T, df_exp_corr)\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.scatterplot(data=df_2d_exp, x='AUC', y='DEGREE_NULL_AUC')\n", "plt.plot([0, 1], [0, 1], c='black')\n", "plt.axvline(x=df_2d_exp['AUC'].mean(),c='black',ls='--')\n", "plt.axhline(y=df_2d_exp['DEGREE_NULL_AUC'].mean(), c='black', ls='--')" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [], "source": [ "df_2d_jac.reset_index(inplace=True)\n", "df_2d_exp.reset_index(inplace=True)\n", "coexp_contact = df_2d_jac.merge(df_2d_exp, left_on='index', right_on='index')" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Immune system-Basophils0.966514686.3318470.3968598.957349e-08
Immune system-Naive CD8+ T cells0.968314821.9508640.5151334.288808e-10
Immune system-γδ-T cells0.968605769.5530200.4643498.119871e-11
Immune system--T cells0.968899769.5530200.4643495.924728e-11
Placenta-Lymphoid cells0.969792713.8325400.4112462.444644e-08
Placenta-Myeloid cells0.973610674.4242800.3806198.182613e-09
Stomach-Lymphoid cells0.974136777.2571230.4673122.445967e-09
Placenta-Megakaryocytes0.975818715.1167470.4169964.939503e-07
Stomach-Squamous epithelial cells0.975893421.0263890.1491511.253213e-05
Heart-Lymphoid cells0.976310680.7265330.3680744.947886e-10
Immune system-Mast cells0.976431838.2486970.5455333.501668e-05
Intestine-Chromaffin cells0.978456351.1125870.0980113.959878e-05
Immune system-Eosinophils0.978604644.9422770.3433045.867062e-04
Adrenal-Erythroblasts0.980016422.4706340.1469792.215531e-06
Placenta-AFP_ALB positive cells0.982184433.9792870.1634721.149528e-07
Adrenal-Lymphoid cells0.983204745.4821430.4336824.165141e-09
Immune system-ISG expressing immune cells0.984182845.9371180.5406463.247684e-07
Intestine-Lymphoid cells0.984755729.6640590.4107821.164714e-06
Muscle-Erythroblasts0.992657456.6582360.1934688.335917e-09
Intestine-Erythroblasts0.997374395.9211330.1226227.362984e-09
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE \\\n", "Immune system-Basophils 0.966514 686.331847 \n", "Immune system-Naive CD8+ T cells 0.968314 821.950864 \n", "Immune system-γδ-T cells 0.968605 769.553020 \n", "Immune system--T cells 0.968899 769.553020 \n", "Placenta-Lymphoid cells 0.969792 713.832540 \n", "Placenta-Myeloid cells 0.973610 674.424280 \n", "Stomach-Lymphoid cells 0.974136 777.257123 \n", "Placenta-Megakaryocytes 0.975818 715.116747 \n", "Stomach-Squamous epithelial cells 0.975893 421.026389 \n", "Heart-Lymphoid cells 0.976310 680.726533 \n", "Immune system-Mast cells 0.976431 838.248697 \n", "Intestine-Chromaffin cells 0.978456 351.112587 \n", "Immune system-Eosinophils 0.978604 644.942277 \n", "Adrenal-Erythroblasts 0.980016 422.470634 \n", "Placenta-AFP_ALB positive cells 0.982184 433.979287 \n", "Adrenal-Lymphoid cells 0.983204 745.482143 \n", "Immune system-ISG expressing immune cells 0.984182 845.937118 \n", "Intestine-Lymphoid cells 0.984755 729.664059 \n", "Muscle-Erythroblasts 0.992657 456.658236 \n", "Intestine-Erythroblasts 0.997374 395.921133 \n", "\n", " DEGREE_NULL_AUC P_Value \n", "Immune system-Basophils 0.396859 8.957349e-08 \n", "Immune system-Naive CD8+ T cells 0.515133 4.288808e-10 \n", "Immune system-γδ-T cells 0.464349 8.119871e-11 \n", "Immune system--T cells 0.464349 5.924728e-11 \n", "Placenta-Lymphoid cells 0.411246 2.444644e-08 \n", "Placenta-Myeloid cells 0.380619 8.182613e-09 \n", "Stomach-Lymphoid cells 0.467312 2.445967e-09 \n", "Placenta-Megakaryocytes 0.416996 4.939503e-07 \n", "Stomach-Squamous epithelial cells 0.149151 1.253213e-05 \n", "Heart-Lymphoid cells 0.368074 4.947886e-10 \n", "Immune system-Mast cells 0.545533 3.501668e-05 \n", "Intestine-Chromaffin cells 0.098011 3.959878e-05 \n", "Immune system-Eosinophils 0.343304 5.867062e-04 \n", "Adrenal-Erythroblasts 0.146979 2.215531e-06 \n", "Placenta-AFP_ALB positive cells 0.163472 1.149528e-07 \n", "Adrenal-Lymphoid cells 0.433682 4.165141e-09 \n", "Immune system-ISG expressing immune cells 0.540646 3.247684e-07 \n", "Intestine-Lymphoid cells 0.410782 1.164714e-06 \n", "Muscle-Erythroblasts 0.193468 8.335917e-09 \n", "Intestine-Erythroblasts 0.122622 7.362984e-09 " ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_exp.sort_values(by=['AUC']).tail(20)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
Adrenal-Erythroblasts0.7440157.830639e+070.4726691.577830e-02
Eye-GABAergic amacrine cells0.7452417.268417e+070.3507121.214904e-02
Stomach-PDE1C_ACSM3 positive cells0.7541327.301663e+070.3741271.073852e-03
Heart-Megakaryocytes0.7543747.470928e+070.4139421.688054e-03
Immune system-Intermediate monocytes0.7553598.918103e+070.6111742.788973e-03
Adrenal-SLC26A4_PAEP positive cells0.7578368.059366e+070.4965351.268912e-02
Eye-Glycinergic amacrine cells0.7584367.143455e+070.3319236.499352e-03
Immune system-Non-classical monocytes0.7652857.983960e+070.5097102.098806e-04
Adrenal-Stromal cells0.7660237.476892e+070.3798775.048967e-03
Spleen-Stromal cells0.7670476.456358e+070.3275213.439434e-03
White adipose tissue-Adipocytes0.7895247.134295e+070.3523231.522735e-03
Adrenal-CSH1_CSH2 positive cells0.8005418.125798e+070.4068558.298341e-05
Brain-Neuroepithelial cells0.8054717.778916e+070.4218122.012711e-03
Immune system-Eosinophils0.8178437.799499e+070.4043083.891490e-04
White adipose tissue-Mesothelial cells0.8422106.219281e+070.1815674.646414e-04
White adipose tissue-Lymphatic Endothelial cells0.8510036.926410e+070.2843521.587523e-07
White adipose tissue-Endothelial cells0.8536516.394237e+070.2583571.073930e-06
White adipose tissue-Adipose progenitor cells0.8796746.533446e+070.3143322.981767e-04
White adipose tissue-Pericytes0.9241435.884099e+070.1513232.336886e-07
White adipose tissue-Endometrium0.9558025.597243e+070.1224482.936266e-08
\n", "
" ], "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]" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [], "source": [ "species= marker_gene_table.T.idxmax(axis=1)" ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Brain-Immune system cells', 'Brain-Microglial cells',\n", " 'Brain-Astrocytes', 'Brain-Oligodendrocytes',\n", " 'Brain-Dopaminergic neurons', 'Brain-GABAergic neurons',\n", " 'Brain-Neuroblasts', 'Brain-Endothelial cells',\n", " 'Brain-Cholinergic neurons', 'Brain-Radial glial cells',\n", " 'Brain-Mature neurons', 'Brain-Neural Progenitor cells',\n", " 'Brain-Immature neurons', 'Brain-Neuroepithelial cells',\n", " 'Brain-Glutamatergic neurons', 'Brain-Tanycytes',\n", " 'Brain-Non myelinating Schwann cells',\n", " 'Brain-Oligodendrocyte precursor cells',\n", " 'Brain-Myelinating Schwann cells', 'Brain-Cancer cells',\n", " 'Brain-Cancer stem cells', 'Brain-Neural stem cells',\n", " 'Brain-Serotonergic neurons', 'Brain-Schwann precursor cells'],\n", " dtype=object)" ] }, "execution_count": 218, "metadata": {}, "output_type": "execute_result" } ], "source": [ "species.unique()" ] }, { "cell_type": "code", "execution_count": 219, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "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 }