{ "cells": [ { "cell_type": "code", "execution_count": 10, "id": "edd28874", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, sparse\n", "import bottleneck\n", "from scipy.stats import mannwhitneyu" ] }, { "cell_type": "code", "execution_count": 11, "id": "29a93616", "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": 12, "id": "cef71417", "metadata": {}, "outputs": [], "source": [ "from hicmatrix import HiCMatrix as hm\n", "from hicmatrix.lib import MatrixFileHandler" ] }, { "cell_type": "code", "execution_count": 13, "id": "9c63a61a", "metadata": {}, "outputs": [], "source": [ "\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, "id": "75a5646c", "metadata": {}, "outputs": [], "source": [ "df_gene_chr = pd.DataFrame(list(zip([x[3].decode() for x in jac_exp.cut_intervals], [x[0] for x in jac_exp.cut_intervals])),\n", " columns =['gene', 'chrom'])" ] }, { "cell_type": "code", "execution_count": 54, "id": "144684bc", "metadata": {}, "outputs": [], "source": [ "df_gene_chr['val'] = 1" ] }, { "cell_type": "code", "execution_count": 56, "id": "25cd86f3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('chr1', 0, 1, b'ENSG00000278267'),\n", " ('chr1', 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('chr1', 0, 1, b'ENSG00000158966'),\n", " ('chr1', 0, 1, b'ENSG00000162437'),\n", " ('chr1', 0, 1, b'ENSG00000162434'),\n", " ('chr1', 0, 1, b'ENSG00000234784'),\n", " ('chr1', 0, 1, b'ENSG00000233877'),\n", " ('chr1', 0, 1, b'ENSG00000226891'),\n", " ('chr1', 0, 1, b'ENSG00000185031'),\n", " ('chr1', 0, 1, b'ENSG00000272506'),\n", " ('chr1', 0, 1, b'ENSG00000265996'),\n", " ('chr1', 0, 1, b'ENSG00000231485'),\n", " ('chr1', 0, 1, b'ENSG00000162433'),\n", " ('chr1', 0, 1, b'ENSG00000116675'),\n", " ('chr1', 0, 1, b'ENSG00000213625'),\n", " ('chr1', 0, 1, b'ENSG00000116678'),\n", " ('chr1', 0, 1, b'ENSG00000184588'),\n", " ('chr1', 0, 1, b'ENSG00000118473'),\n", " ('chr1', 0, 1, b'ENSG00000248458'),\n", " ('chr1', 0, 1, b'ENSG00000152760'),\n", " ('chr1', 0, 1, b'ENSG00000152763'),\n", " ('chr1', 0, 1, b'ENSG00000198160'),\n", " ('chr1', 0, 1, b'ENSG00000116704'),\n", " ('chr1', 0, 1, b'ENSG00000275678'),\n", " ('chr1', 0, 1, b'ENSG00000162594'),\n", " ('chr1', 0, 1, b'ENSG00000081985'),\n", " ('chr1', 0, 1, b'ENSG00000142864'),\n", " ('chr1', 0, 1, b'ENSG00000223263'),\n", " ('chr1', 0, 1, b'ENSG00000116717'),\n", " ('chr1', 0, 1, b'ENSG00000172380'),\n", " ('chr1', 0, 1, b'ENSG00000232284'),\n", " ('chr1', 0, 1, b'ENSG00000162595'),\n", " ('chr1', 0, 1, b'ENSG00000116729'),\n", " ('chr1', 0, 1, b'ENSG00000234383'),\n", " ('chr1', 0, 1, b'ENSG00000229133'),\n", " ('chr1', 0, 1, b'ENSG00000116745'),\n", " ('chr1', 0, 1, b'ENSG00000024526'),\n", " ('chr1', 0, 1, b'ENSG00000033122'),\n", " ('chr1', 0, 1, b'ENSG00000066557'),\n", " ('chr1', 0, 1, b'ENSG00000116754'),\n", " ('chr1', 0, 1, b'ENSG00000118454'),\n", " ('chr1', 0, 1, b'ENSG00000197568'),\n", " ('chr1', 0, 1, b'ENSG00000116761'),\n", " ('chr1', 0, 1, b'ENSG00000271992'),\n", " ('chr1', 0, 1, b'ENSG00000050628'),\n", " ('chr1', 0, 1, b'ENSG00000235079'),\n", " ('chr1', 0, 1, b'ENSG00000132485'),\n", " ('chr1', 0, 1, b'ENSG00000207721'),\n", " ('chr1', 0, 1, b'ENSG00000229956'),\n", " ('chr1', 0, 1, b'ENSG00000172260'),\n", " ('chr1', 0, 1, b'ENSG00000233994'),\n", " ('chr1', 0, 1, b'ENSG00000162620'),\n", " ('chr1', 0, 1, b'ENSG00000254685'),\n", " ('chr1', 0, 1, b'ENSG00000259030'),\n", " ('chr1', 0, 1, b'ENSG00000116783'),\n", " ('chr1', 0, 1, b'ENSG00000162621'),\n", " ('chr1', 0, 1, b'ENSG00000178965'),\n", " ('chr1', 0, 1, b'ENSG00000272864'),\n", " ('chr1', 0, 1, b'ENSG00000116791'),\n", " ('chr1', 0, 1, b'ENSG00000162623'),\n", " ('chr1', 0, 1, b'ENSG00000137968'),\n", " ('chr1', 0, 1, b'ENSG00000117054'),\n", " ('chr1', 0, 1, b'ENSG00000181227'),\n", " ('chr1', 0, 1, b'ENSG00000137955'),\n", " ('chr1', 0, 1, b'ENSG00000057468'),\n", " ('chr1', 0, 1, b'ENSG00000184005'),\n", " ('chr1', 0, 1, b'ENSG00000226415'),\n", " ('chr1', 0, 1, b'ENSG00000117069'),\n", " ('chr1', 0, 1, b'ENSG00000230498'),\n", " ('chr1', 0, 1, b'ENSG00000142892'),\n", " ('chr1', 0, 1, b'ENSG00000226084'),\n", " ('chr1', 0, 1, b'ENSG00000154027'),\n", " ('chr1', 0, 1, b'ENSG00000036549'),\n", " ('chr1', 0, 1, b'ENSG00000077254'),\n", " ('chr1', 0, 1, b'ENSG00000180488'),\n", " ('chr1', 0, 1, b'ENSG00000219201'),\n", " ('chr1', 0, 1, b'ENSG00000235613'),\n", " ('chr1', 0, 1, b'ENSG00000235927'),\n", " ('chr1', 0, 1, b'ENSG00000162614'),\n", " ('chr1', 0, 1, b'ENSG00000162613'),\n", " ('chr1', 0, 1, b'ENSG00000162616'),\n", " ('chr1', 0, 1, b'ENSG00000137960'),\n", " ('chr1', 0, 1, b'ENSG00000273338'),\n", " ('chr1', 0, 1, b'ENSG00000122420'),\n", " ...]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jac_exp.cut_intervals" ] }, { "cell_type": "code", "execution_count": 52, "id": "89d45039", "metadata": {}, "outputs": [], "source": [ "df_gene_chr = df_gene_chr.pivot_table(index='gene', columns='chrom', values='val', aggfunc='sum')" ] }, { "cell_type": "code", "execution_count": 15, "id": "ec5321dc", "metadata": {}, "outputs": [], "source": [ "df_gene_chr = df_gene_chr.fillna(0)" ] }, { "cell_type": "code", "execution_count": 17, "id": "364fa61b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(24243, 24243)\n", "(24243, 23)\n", "0.9565217391304348\n", "0.0\n" ] } ], "source": [ "df_2d_jac, go_chrom = run_egad(df_gene_chr, df_exp_corr)" ] }, { "cell_type": "code", "execution_count": 1, "id": "3f27268e", "metadata": {}, "outputs": [], "source": [ "resolution_human = 10000\n", "species = \"human\"\n", "SRP_name = \"aggregates\"\n", "resolution = \"10kbp_raw\"" ] }, { "cell_type": "code", "execution_count": 42, "id": "aa3f2647", "metadata": {}, "outputs": [], "source": [ " input_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/'\n", " bins_bed = pd.read_csv(f'{input_path}/all_bins.bed', names=['chr', 'start', 'end', 'bin_id'])" ] }, { "cell_type": "code", "execution_count": 45, "id": "0b9ea825", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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chrstartendbin_id
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...............
287504chr2250770000507800001
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287509 rows × 4 columns

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" ], "text/plain": [ " chr start end bin_id\n", "0 chr1 0 10000 1\n", "1 chr1 10000 20000 1\n", "2 chr1 20000 30000 1\n", "3 chr1 30000 40000 1\n", "4 chr1 40000 50000 1\n", "... ... ... ... ...\n", "287504 chr22 50770000 50780000 1\n", "287505 chr22 50780000 50790000 1\n", "287506 chr22 50790000 50800000 1\n", "287507 chr22 50800000 50810000 1\n", "287508 chr22 50810000 50818468 1\n", "\n", "[287509 rows x 4 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bins_bed" ] }, { "cell_type": "code", "execution_count": 46, "id": "fc460700", "metadata": {}, "outputs": [], "source": [ "df_auc_vs_size = bins_bed.groupby(['chr'])['bin_id'].sum().reset_index().merge(df_2d_jac, left_on='chr', right_on=df_2d_jac.index)\n" ] }, { "cell_type": "code", "execution_count": 47, "id": "492c6b17", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 57, "id": "a75ad408", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 57, "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(x=df_auc_vs_size['bin_id'], y=df_auc_vs_size['AUC'])" ] }, { "cell_type": "code", "execution_count": 58, "id": "759b63fb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.625168691796963" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_auc_vs_size['AUC'].mean()" ] }, { "cell_type": "code", "execution_count": 38, "id": "930911c7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AUCAVG_NODE_DEGREEDEGREE_NULL_AUCP_Value
chrom
chr10.54808612339.8425090.5001631.654169e-16
chr100.58647912438.5985100.5095001.143218e-20
chr110.57429712373.5963950.5047591.818544e-20
chr120.55637512376.4064480.5046628.466436e-13
chr130.66705612156.2594540.4917091.109736e-36
chr140.56251412193.3423820.4972777.912834e-10
chr150.59454912143.0865160.4878641.052527e-21
chr160.70618312335.1618540.4995468.773301e-126
chr170.66692312595.4759120.5164801.190610e-102
chr180.66201211630.0051050.4588881.417273e-29
chr190.74335812796.6384580.5294965.206787e-237
chr20.58304712288.5386490.5012772.970102e-31
chr200.62671612350.8596760.5066642.054682e-29
chr210.60678311578.9427050.4515345.082539e-10
chr220.74089012264.4624110.4931581.192274e-92
chr30.58067812552.5419170.5187264.347483e-25
chr40.68801312121.8248320.4870913.659621e-87
chr50.62056611982.8178310.4794124.853560e-45
chr60.63683011747.7678830.4672784.285290e-56
chr70.59375512443.3406730.5071294.118314e-29
chr80.63434811803.7157310.4656174.540686e-41
chr90.57425312519.5249110.5135622.091948e-15
chrX0.69307812553.5424690.5137105.165830e-77
\n", "
" ], "text/plain": [ " AUC AVG_NODE_DEGREE DEGREE_NULL_AUC P_Value\n", "chrom \n", "chr1 0.548086 12339.842509 0.500163 1.654169e-16\n", "chr10 0.586479 12438.598510 0.509500 1.143218e-20\n", "chr11 0.574297 12373.596395 0.504759 1.818544e-20\n", "chr12 0.556375 12376.406448 0.504662 8.466436e-13\n", "chr13 0.667056 12156.259454 0.491709 1.109736e-36\n", "chr14 0.562514 12193.342382 0.497277 7.912834e-10\n", "chr15 0.594549 12143.086516 0.487864 1.052527e-21\n", "chr16 0.706183 12335.161854 0.499546 8.773301e-126\n", "chr17 0.666923 12595.475912 0.516480 1.190610e-102\n", "chr18 0.662012 11630.005105 0.458888 1.417273e-29\n", "chr19 0.743358 12796.638458 0.529496 5.206787e-237\n", "chr2 0.583047 12288.538649 0.501277 2.970102e-31\n", "chr20 0.626716 12350.859676 0.506664 2.054682e-29\n", "chr21 0.606783 11578.942705 0.451534 5.082539e-10\n", "chr22 0.740890 12264.462411 0.493158 1.192274e-92\n", "chr3 0.580678 12552.541917 0.518726 4.347483e-25\n", "chr4 0.688013 12121.824832 0.487091 3.659621e-87\n", "chr5 0.620566 11982.817831 0.479412 4.853560e-45\n", "chr6 0.636830 11747.767883 0.467278 4.285290e-56\n", "chr7 0.593755 12443.340673 0.507129 4.118314e-29\n", "chr8 0.634348 11803.715731 0.465617 4.540686e-41\n", "chr9 0.574253 12519.524911 0.513562 2.091948e-15\n", "chrX 0.693078 12553.542469 0.513710 5.165830e-77" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_2d_jac" ] }, { "cell_type": "code", "execution_count": 6, "id": "0fa5b90d", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 7, "id": "0e2800da", "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" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\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 sns.clustermap(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf_exp_corr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\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 3\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\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 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\u001b[0mcolorbar_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcbar_kws\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1408\u001b[0m \u001b[0mrow_cluster\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrow_cluster\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_cluster\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol_cluster\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/seaborn/matrix.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, metric, method, colorbar_kws, row_cluster, col_cluster, row_linkage, col_linkage, tree_kws, **kws)\u001b[0m\n\u001b[1;32m 1217\u001b[0m \u001b[0mcolorbar_kws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolorbar_kws\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcolorbar_kws\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1219\u001b[0;31m self.plot_dendrograms(row_cluster, col_cluster, metric, method,\n\u001b[0m\u001b[1;32m 1220\u001b[0m \u001b[0mrow_linkage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrow_linkage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_linkage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol_linkage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1221\u001b[0m tree_kws=tree_kws)\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36mplot_dendrograms\u001b[0;34m(self, row_cluster, col_cluster, metric, method, row_linkage, col_linkage, tree_kws)\u001b[0m\n\u001b[1;32m 1072\u001b[0m \u001b[0;31m# PLot the column dendrogram\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1073\u001b[0m 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657\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcalculate_dendrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/seaborn/matrix.py\u001b[0m in \u001b[0;36m_calculate_linkage_scipy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_calculate_linkage_scipy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 624\u001b[0;31m linkage = hierarchy.linkage(self.array, method=self.method,\n\u001b[0m\u001b[1;32m 625\u001b[0m metric=self.metric)\n\u001b[1;32m 626\u001b[0m \u001b[0;32mreturn\u001b[0m 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"\u001b[0;32m_hierarchy.pyx\u001b[0m in \u001b[0;36mscipy.cluster._hierarchy.nn_chain\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36margsort\u001b[0;34m(*args, **kwargs)\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(df_exp_corr.head(1000))\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "2d978f0a", "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[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_exp_corr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'hot'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'df_exp_corr' is not defined" ] } ], "source": [ "plt.imshow(df_exp_corr.head(10), cmap='hot')" ] }, { "cell_type": "code", "execution_count": 5, "id": "c9f592d7", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "id": "2c95973a", "metadata": {}, "outputs": [], "source": [ "plt.savefig('test.png', dpi=600)" ] }, { "cell_type": "code", "execution_count": null, "id": "c3c8b790", "metadata": {}, "outputs": [], "source": [ "plt.savefig('test.png', dpi=600)\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "b64fad3f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:numexpr.utils:Note: detected 192 virtual cores but NumExpr set to maximum of 64, check \"NUMEXPR_MAX_THREADS\" environment variable.\n", "INFO:numexpr.utils:Note: NumExpr detected 192 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" ] } ], "source": [ "from hicmatrix import HiCMatrix as hm\n", "from hicmatrix.lib import MatrixFileHandler\n", "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, sparse\n", "import bottleneck\n", "from scipy.stats import mannwhitneyu\n", "import h5py\n", "import h5py\n", "import logging\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 scipy.sparse import csr_matrix, dia_matrix, triu, tril, coo_matrix\n", "import scipy.stats as stats\n", "import os.path" ] }, { "cell_type": "code", "execution_count": 3, "id": "bbbe45df", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "\"Unable to open object (object 'matrix' doesn't exist)\"", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\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 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/hic_gene_gw_none_by_allbins_none.h5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mhf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmy_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'matrix'\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mgene_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gene_list'\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mbins_bed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bins_bed'\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[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/h5py/_hl/group.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid HDF5 object reference\"\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[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m \u001b[0moid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_e\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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h5py.File(f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/hic_gene_gw_none_by_allbins_none.h5', 'r') as hf:\n", " my_data = hf['matrix'][:]\n", " gene_list = hf['gene_list'][:]\n", " bins_bed = hf['bins_bed'][:]" ] }, { "cell_type": "code", "execution_count": null, "id": "fd409eca", "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": 5 }