{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "7a17e37d", "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": 2, "id": "ec1b3f2d", "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=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": 3, "id": "c22d0bb1", "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" ] }, { "cell_type": "code", "execution_count": 4, "id": "7e042736", "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)\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "841af40f", "metadata": {}, "outputs": [], "source": [ "resolution_human = 1000\n", "species = \"human\"\n", "SRP_name = \"aggregates\"\n", "resolution = \"1kbp_raw\"\n", "\n", "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/spr/hic_gene_corr_inter_excluding_intra_chrom_pairs_hicexp.h5'\n", "\n", "jac_exp = hm.hiCMatrix(exp_file_path)\n", "\n", "all_genes = [x[3].decode() for x in jac_exp.cut_intervals]\n", "\n", "\n", "f_m = jac_exp.matrix.toarray()\n", "\n", "f_m = f_m + abs(f_m.min())\n", "\n", "np.fill_diagonal(f_m, 0)\n", "\n", "df_exp_corr = pd.DataFrame(f_m , index=all_genes, columns = all_genes)\n", " " ] }, { "cell_type": "code", "execution_count": 6, "id": "08d250cc", "metadata": {}, "outputs": [], "source": [ "import scipy.spatial as sp, scipy.cluster.hierarchy as hc\n", "df_exp_corr_pdist = sp.distance.squareform(df_exp_corr)" ] }, { "cell_type": "code", "execution_count": 7, "id": "cf055c88", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.cluster import hierarchy as hc\n", "import scipy.spatial as sp, scipy.cluster.hierarchy as hc\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 8, "id": "c2c6ac65", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.cluster import hierarchy as hc\n", "import matplotlib.pyplot as plt\n", " \n", "# Create an array\n", "x = np.array([[0,0.3,0.4],[0.3,0,0.7],[0.4,0.7,0]])\n", " \n", "# Plot the hierarchical clustering as a dendrogram.\n", "\n", "temp = hc.linkage(df_exp_corr_pdist, method='average')" ] }, { "cell_type": "code", "execution_count": 78, "id": "1ee3ab52", "metadata": {}, "outputs": [], "source": [ "from scipy.cluster.hierarchy import fcluster\n", "fl = fcluster(temp,0.05,criterion='distance')" ] }, { "cell_type": "code", "execution_count": 79, "id": "eea91cbc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "14914" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(list(np.unique(fl)))" ] }, { "cell_type": "code", "execution_count": 80, "id": "6f4b1bd9", "metadata": {}, "outputs": [], "source": [ "from collections import Counter\n", "z = list(fl)\n", "z = Counter(z)" ] }, { "cell_type": "code", "execution_count": 81, "id": "6808ae29", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8538" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(z.values())" ] }, { "cell_type": "code", "execution_count": 82, "id": "dcbbfa1f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len([x for x in z.values() if x > 2])" ] }, { "cell_type": "code", "execution_count": 31, "id": "ae2d907b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_values([1564, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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"3e0c808a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR:root:Internal Python error in the inspect module.\n", "Below is the traceback from this internal error.\n", "\n", "INFO:root:\n", "Unfortunately, your original traceback can not be constructed.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Traceback (most recent call last):\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/indexes/base.py\", line 3080, in get_loc\n", " return self._engine.get_loc(casted_key)\n", " File \"pandas/_libs/index.pyx\", line 70, in pandas._libs.index.IndexEngine.get_loc\n", " File \"pandas/_libs/index.pyx\", line 101, in pandas._libs.index.IndexEngine.get_loc\n", " File \"pandas/_libs/hashtable_class_helper.pxi\", line 4554, in pandas._libs.hashtable.PyObjectHashTable.get_item\n", " File \"pandas/_libs/hashtable_class_helper.pxi\", line 4562, in pandas._libs.hashtable.PyObjectHashTable.get_item\n", "KeyError: 693\n", "\n", "The above exception was the direct cause of the following exception:\n", "\n", "Traceback (most recent call last):\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/generic.py\", line 3826, in _set_item\n", " loc = self._info_axis.get_loc(key)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/indexes/base.py\", line 3082, in get_loc\n", " raise KeyError(key) from err\n", "KeyError: 693\n", "\n", "During handling of the above exception, another exception occurred:\n", "\n", "Traceback (most recent call last):\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3441, in run_code\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", " File \"\", line 6, in \n", " df_exp_clust[clust] = [1 if x == clust else 0 for x in df_exp_clust['label']]\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/frame.py\", line 3163, in __setitem__\n", " self._set_item(key, value)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/frame.py\", line 3243, in _set_item\n", " NDFrame._set_item(self, key, value)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/generic.py\", line 3829, in _set_item\n", " self._mgr.insert(len(self._info_axis), key, value)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/internals/managers.py\", line 1229, in insert\n", " self._consolidate_inplace()\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/internals/managers.py\", line 993, in _consolidate_inplace\n", " self.blocks = tuple(_consolidate(self.blocks))\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/internals/managers.py\", line 1916, in _consolidate\n", " merged_blocks = _merge_blocks(\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/internals/managers.py\", line 1942, in _merge_blocks\n", " new_values = new_values[argsort]\n", "KeyboardInterrupt\n", "\n", "During handling of the above exception, another exception occurred:\n", "\n", "Traceback (most recent call last):\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 2061, in showtraceback\n", " stb = value._render_traceback_()\n", "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", "\n", "During handling of the above exception, another exception occurred:\n", "\n", "Traceback (most recent call last):\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n", " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n", " return f(*args, **kwargs)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n", " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/inspect.py\", line 1515, in getinnerframes\n", " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/inspect.py\", line 1473, in getframeinfo\n", " filename = getsourcefile(frame) or getfile(frame)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/inspect.py\", line 708, in getsourcefile\n", " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/inspect.py\", line 751, in getmodule\n", " f = getabsfile(module)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/inspect.py\", line 720, in getabsfile\n", " _filename = getsourcefile(object) or getfile(object)\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/inspect.py\", line 705, in getsourcefile\n", " if os.path.exists(filename):\n", " File \"/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/genericpath.py\", line 19, in exists\n", " os.stat(path)\n", "KeyboardInterrupt\n" ] }, { "ename": "TypeError", "evalue": "object of type 'NoneType' has no len()", "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: 693", "\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~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3825\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-> 3826\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\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 3827\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\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 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[0;31mKeyError\u001b[0m: 693", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mclust\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfl\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdf_exp_clust\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclust\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mclust\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf_exp_clust\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3162\u001b[0m \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3163\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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 3164\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_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3242\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3243\u001b[0;31m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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 3244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3828\u001b[0m \u001b[0;31m# This item wasn't present, just insert at end\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3829\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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 3830\u001b[0m \u001b[0;32mreturn\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/internals/managers.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, loc, item, value, allow_duplicates)\u001b[0m\n\u001b[1;32m 1228\u001b[0m \u001b[0;32mif\u001b[0m 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993\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_consolidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\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 994\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_consolidated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\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/internals/managers.py\u001b[0m in \u001b[0;36m_consolidate\u001b[0;34m(blocks)\u001b[0m\n\u001b[1;32m 1915\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_can_consolidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_blocks\u001b[0m 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\u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m 2060\u001b[0m \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2061\u001b[0;31m \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\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 2062\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m 2061\u001b[0m \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\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 2062\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2063\u001b[0;31m stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0m\u001b[1;32m 2064\u001b[0m value, tb, tb_offset=tb_offset)\n\u001b[1;32m 2065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m 1365\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[1;32m 1366\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m return FormattedTB.structured_traceback(\n\u001b[0m\u001b[1;32m 1368\u001b[0m self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[1;32m 1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m 1265\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose_modes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1266\u001b[0m \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m return VerboseTB.structured_traceback(\n\u001b[0m\u001b[1;32m 1268\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1269\u001b[0m )\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m 1122\u001b[0m \u001b[0;34m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0m\u001b[1;32m 1125\u001b[0m tb_offset)\n\u001b[1;32m 1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m 1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\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 1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1084\u001b[0m \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\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/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0;31m# first frame (from in to out) that looks different.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\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--> 382\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\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[0m\u001b[1;32m 383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0;31m# Select filename, lineno, func_name to track frames with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()" ] } ], "source": [ "df_exp_clust = pd.DataFrame(fl , index=all_genes)\n", "\n", "df_exp_clust['label'] = df_exp_clust[0]\n", "\n", "for clust in list(range(0,len(list(np.unique(fl))))):\n", " df_exp_clust[clust] = [1 if x == clust else 0 for x in df_exp_clust['label']]" ] }, { "cell_type": "code", "execution_count": 12, "id": "92d87cb2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "label 1759781\n", "1 21602\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_exp_clust.sum()[df_exp_clust.sum() > 2]" ] }, { "cell_type": "code", "execution_count": 50, "id": "2585e33d", "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.setrecursionlimit(100000)" ] }, { "cell_type": "code", "execution_count": null, "id": "3ba89f6b", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import seaborn as sns\n", "import numpy as np\n", "from scipy.cluster import hierarchy\n", "import matplotlib.pyplot as plt\n", "dn = hierarchy.dendrogram(\n", " temp, above_threshold_color=\"green\", color_threshold=0.2)" ] }, { "cell_type": "code", "execution_count": 28, "id": "65aeff26", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.07460000e+04, 2.26250000e+04, -2.97134556e-03,\n", " 2.00000000e+00],\n", " [ 8.26400000e+03, 9.49000000e+03, -1.57280487e-03,\n", " 2.00000000e+00],\n", " [ 3.16000000e+03, 1.77720000e+04, -1.55369169e-03,\n", " 2.00000000e+00],\n", " ...,\n", " [ 4.49900000e+03, 4.69250000e+04, 2.43343432e-01,\n", " 2.34630000e+04],\n", " [ 5.58400000e+03, 4.69260000e+04, 2.43646048e-01,\n", " 2.34640000e+04],\n", " [ 5.58600000e+03, 4.69270000e+04, 2.46495230e-01,\n", " 2.34650000e+04]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp" ] }, { "cell_type": "code", "execution_count": null, "id": "5ce25a63", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "04dd68be", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "id": "903ff957", "metadata": {}, "outputs": [], "source": [ "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/spr/hic_gene_corr_inter_excluding_intra_chrom_pairs_hicexp.h5'\n", "\n", "jac_exp = hm.hiCMatrix(exp_file_path)\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "85e85a05", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jac_exp " ] }, { "cell_type": "code", "execution_count": 5, "id": "ee13dfc3", "metadata": {}, "outputs": [], "source": [ "df_exp_corr = pd.DataFrame(jac_exp.matrix.toarray() , index=all_genes, columns = all_genes)" ] }, { "cell_type": "code", "execution_count": 10, "id": "7503b21d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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... \n", "ENSG00000155959 0.634972 0.973678 \n", "ENSG00000155961 0.441307 0.732325 \n", "ENSG00000155962 0.511023 0.850492 \n", "ENSG00000224533 1.000000 0.762474 \n", "ENSG00000185973 0.762474 1.000000 \n", "\n", "[24243 rows x 24243 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_exp_corr " ] }, { "cell_type": "code", "execution_count": null, "id": "ff9e72fe", "metadata": {}, "outputs": [], "source": [ "import scipy.spatial as sp, scipy.cluster.hierarchy as hc\n", "df_exp_corr_pdist = sp.distance.squareform(df_exp_corr)" ] }, { "cell_type": "code", "execution_count": null, "id": "65744e01", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "id": "221625ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AgglomerativeClustering(affinity='precomputed', linkage='single')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np \n", "from sklearn.cluster import AgglomerativeClustering\n", "\n", "X = np.array([[0,0.3,0.4],[0.3,0,0.7],[0.4,0.7,0]])\n", "\n", "#cluster = AgglomerativeClustering(affinity='precomputed', linkage='complete') \n", "#cluster = AgglomerativeClustering(affinity='precomputed', linkage='average')\n", "cluster = AgglomerativeClustering(affinity='precomputed', linkage='single')\n", "cluster.fit(X)" ] }, { "cell_type": "code", "execution_count": 18, "id": "0071875f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.3, 0.4, 0.7])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 13, "id": "51eabb75", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import seaborn as sns\n", "import numpy as np\n", "from scipy.cluster import hierarchy\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 33, "id": "baa0726f", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from scipy.cluster import hierarchy as hc\n", "import matplotlib.pyplot as plt\n", " \n", "# Create an array\n", "x = np.array([[0,0.3,0.4],[0.3,0,0.7],[0.4,0.7,0]])\n", " \n", "# Plot the hierarchical clustering as a dendrogram.\n", "\n", "temp = hc.linkage(sp.distance.squareform(x), method='average')\n", "plt.figure()\n", " \n", "dn = hierarchy.dendrogram(\n", " temp, above_threshold_color=\"green\", color_threshold=0.5)" ] }, { "cell_type": "code", "execution_count": 36, "id": "e0ba1a25", "metadata": {}, "outputs": [], "source": [ "from scipy.cluster.hierarchy import fcluster\n", "fl = fcluster(temp,0.5,criterion='distance')" ] }, { "cell_type": "code", "execution_count": 37, "id": "22dc51de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 2], dtype=int32)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fl" ] }, { "cell_type": "code", "execution_count": 25, "id": "0cde9b6e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "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.clustermap(x, row_linkage=linkage, col_linkage=linkage, cmap=\"vlag\")" ] }, { "cell_type": "code", "execution_count": 20, "id": "aa8cf0fc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0. , 1. , 0.3 , 2. ],\n", " [2. , 3. , 0.55, 3. ]])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp" ] }, { "cell_type": "code", "execution_count": null, "id": "c9b089e3", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.cluster import hierarchy as hc\n", "import matplotlib.pyplot as plt\n", " \n", "# Create an array\n", "x = np.array([[0,0.3,0.4],[0.3,0,0.7],[0.4,0.7,0]])\n", " \n", "# Plot the hierarchical clustering as a dendrogram.\n", "\n", "temp = hc.linkage(x, method='average')\n", "plt.figure()\n", " \n", "dn = hierarchy.dendrogram(\n", " temp, above_threshold_color=\"green\", color_threshold=.7)" ] }, { "cell_type": "code", "execution_count": 17, "id": "112d3f26", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0. , 1. , 0.51961524, 2. ],\n", " [2. , 3. , 0.84390388, 3. ]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp" ] }, { "cell_type": "code", "execution_count": 6, "id": "fbfe779b", "metadata": {}, "outputs": [], "source": [ "resolution_human = 10000\n", "species = \"human\"\n", "SRP_name = \"aggregates\"\n", "resolution = \"10kbp_raw\"\n", "\n", "\n", "\n", "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": 7, "id": "74af0c9a", "metadata": {}, "outputs": [], "source": [ " if species == 'human':\n", "\n", " df_cre = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/aggregates/li2022/GRCh38-cCREs.bed', sep='\\t', names=['chr', 'start', 'end', 't1', 't2', 't3'])\n", "\n", " else:\n", "\n", " df_cre = pd.read_csv('/grid/gillis/data/lohia/ATAC_Risa/mm10-cCREs.bed', sep='\\t', names=['chr', 'start', 'end', 't1', 't2', 't3'])\n", "\n", " df_cre['start_bin'] = df_cre['start']/resolution_human\n", " df_cre['start_bin'] = df_cre['start_bin'].astype('int')\n", " df_cre['start_bin'] = df_cre['start_bin']*resolution_human\n", " df_cre['start_bin'] = df_cre['start_bin'].astype('str')\n", " df_cre['start_bin'] = df_cre['chr'] + '_' + df_cre['start_bin']\n", " #df_cre_1kb_encode = df_cre.drop_duplicates(subset=['start_bin'])\n", " df_cre['cre'] = 1\n", " df_cre = df_cre.groupby(['start_bin'])['cre'].sum().reset_index()\n", " 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'])\n", " bins_bed['bin_id'] = bins_bed.index\n", " bins_bed['pos'] = bins_bed['chr'] + '_' + bins_bed['start'].astype('str')\n", " df_cre_1kb_encode = df_cre.merge(bins_bed, left_on='start_bin', right_on='pos')" ] }, { "cell_type": "code", "execution_count": 8, "id": "ea98d40b", "metadata": {}, "outputs": [], "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": 9, "id": "74809870", "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 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"\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 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"\u001b[0;31mKeyError\u001b[0m: \"Unable to open object (object 'matrix' doesn't exist)\"" ] } ], "source": [ "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/hic_gene_gw_none_by_allbins_none.h5'\n", "\n", "jac_exp = hm.hiCMatrix(exp_file_path)\n", "\n", "all_genes = [x[3].decode() for x in jac_exp.cut_intervals]\n", "\n", "df_exp_corr = pd.DataFrame(jac_exp.matrix.toarray() , index=all_genes, columns = all_genes)\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "id": "cc07d538", "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 }