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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## TCGA/mutations exploratory data analysis \n",
+ "\n",
+ "We want to know how many genes/cancer types pass our mutation filters (>5% mutated, >50 samples mutated)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ "import pancancer_evaluation.config as cfg\n",
+ "import pancancer_evaluation.utilities.data_utilities as du\n",
+ "\n",
+ "np.random.seed(cfg.default_seed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we load the relevant TCGA preprocessed data, downloaded in the `00_download_data.ipynb` script."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Loading gene label data...\n",
+ "Loading sample info...\n",
+ "Loading pan-cancer data from cached pickle file...\n",
+ "Loading gene expression data...\n",
+ "Standardizing columns of expression data...\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Loading gene label data...', file=sys.stderr)\n",
+ "genes_df = du.load_top_50()\n",
+ "sample_info_df = du.load_sample_info(verbose=True)\n",
+ "\n",
+ "# this returns a tuple of dataframes, unpack it below\n",
+ "pancancer_data = du.load_pancancer_data(verbose=True)\n",
+ "(sample_freeze_df,\n",
+ " mutation_df,\n",
+ " copy_loss_df,\n",
+ " copy_gain_df,\n",
+ " mut_burden_df) = pancancer_data\n",
+ "\n",
+ "rnaseq_df = du.load_expression_data(verbose=True)\n",
+ "\n",
+ "# standardize columns of expression dataframe\n",
+ "print('Standardizing columns of expression data...', file=sys.stderr)\n",
+ "rnaseq_df[rnaseq_df.columns] = StandardScaler().fit_transform(rnaseq_df[rnaseq_df.columns])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(11060, 16148)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 1 | \n",
+ " 10 | \n",
+ " 100 | \n",
+ " 1000 | \n",
+ " 10000 | \n",
+ "
\n",
+ " \n",
+ " sample_id | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " TCGA-02-0047-01 | \n",
+ " -0.144100 | \n",
+ " -0.136450 | \n",
+ " -0.207065 | \n",
+ " 1.049402 | \n",
+ " 0.644625 | \n",
+ "
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+ " \n",
+ " TCGA-02-0055-01 | \n",
+ " -0.124925 | \n",
+ " -0.197893 | \n",
+ " -0.132694 | \n",
+ " 0.704438 | \n",
+ " 0.154763 | \n",
+ "
\n",
+ " \n",
+ " TCGA-02-2483-01 | \n",
+ " -0.133543 | \n",
+ " -0.174587 | \n",
+ " -0.103291 | \n",
+ " 1.473420 | \n",
+ " 0.669303 | \n",
+ "
\n",
+ " \n",
+ " TCGA-02-2485-01 | \n",
+ " -0.147052 | \n",
+ " -0.072888 | \n",
+ " -0.213119 | \n",
+ " 4.405612 | \n",
+ " 11.503035 | \n",
+ "
\n",
+ " \n",
+ " TCGA-02-2486-01 | \n",
+ " -0.145321 | \n",
+ " -0.181076 | \n",
+ " -0.147395 | \n",
+ " 1.013468 | \n",
+ " 0.117745 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 1 10 100 1000 10000\n",
+ "sample_id \n",
+ "TCGA-02-0047-01 -0.144100 -0.136450 -0.207065 1.049402 0.644625\n",
+ "TCGA-02-0055-01 -0.124925 -0.197893 -0.132694 0.704438 0.154763\n",
+ "TCGA-02-2483-01 -0.133543 -0.174587 -0.103291 1.473420 0.669303\n",
+ "TCGA-02-2485-01 -0.147052 -0.072888 -0.213119 4.405612 11.503035\n",
+ "TCGA-02-2486-01 -0.145321 -0.181076 -0.147395 1.013468 0.117745"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(rnaseq_df.shape)\n",
+ "rnaseq_df.iloc[:5, :5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Genes with mutation information: 19660\n"
+ ]
+ }
+ ],
+ "source": [
+ "mutation_genes = set(mutation_df.columns)\n",
+ "copy_loss_genes = set(copy_loss_df.columns)\n",
+ "copy_gain_genes = set(copy_gain_df.columns)\n",
+ "overlap_genes = mutation_genes.intersection(copy_loss_genes.intersection(copy_gain_genes))\n",
+ "print('Genes with mutation information: {}'.format(len(overlap_genes)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total cancer type/mutation combinations: 648780\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " cancertype | \n",
+ " n = | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " BRCA | \n",
+ " 1218 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " KIRC | \n",
+ " 606 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " LUAD | \n",
+ " 576 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " THCA | \n",
+ " 572 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " UCEC | \n",
+ " 567 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " cancertype n =\n",
+ "0 BRCA 1218\n",
+ "1 KIRC 606\n",
+ "2 LUAD 576\n",
+ "3 THCA 572\n",
+ "4 UCEC 567"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cancer_types_df = pd.read_csv(\n",
+ " Path(cfg.data_dir, 'tcga_sample_counts.tsv').resolve(),\n",
+ " sep='\\t'\n",
+ ")\n",
+ "print('Total cancer type/mutation combinations: {}'.format(cancer_types_df.shape[0] * len(overlap_genes)))\n",
+ "cancer_types_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we filter gene/cancer type combinations by the number and proportion of mutations, and put the results in a dataframe.\n",
+ "\n",
+ "For now we're just doing this for the top 50 most mutated genes in TCGA (see `load_top_50` function in `data_utilities.py`), but in the future we could extend this to other gene sets or all genes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " disease | \n",
+ " status | \n",
+ " disease_included | \n",
+ " count | \n",
+ " gene | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " ACC | \n",
+ " 0.197368 | \n",
+ " False | \n",
+ " 15 | \n",
+ " TP53 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " BLCA | \n",
+ " 0.492462 | \n",
+ " True | \n",
+ " 196 | \n",
+ " TP53 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " BRCA | \n",
+ " 0.348624 | \n",
+ " True | \n",
+ " 342 | \n",
+ " TP53 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " CESC | \n",
+ " 0.080882 | \n",
+ " True | \n",
+ " 22 | \n",
+ " TP53 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " CHOL | \n",
+ " 0.111111 | \n",
+ " False | \n",
+ " 4 | \n",
+ " TP53 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " disease status disease_included count gene\n",
+ "0 ACC 0.197368 False 15 TP53\n",
+ "1 BLCA 0.492462 True 196 TP53\n",
+ "2 BRCA 0.348624 True 342 TP53\n",
+ "3 CESC 0.080882 True 22 TP53\n",
+ "4 CHOL 0.111111 False 4 TP53"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def filter_cancer_types(gene, y_df, sample_freeze, mutation_burden):\n",
+ " # most of this code is copied from process_y_matrix in pancancer_utilities.tcga_utilities\n",
+ " # \n",
+ " # note this is not including copy number variants, to do that we have to\n",
+ " # know oncogene/TSG status for every gene (need to figure out where to get\n",
+ " # this info)\n",
+ " y_df = pd.DataFrame(y_df)\n",
+ " y_df.columns = ['status']\n",
+ " y_df = (\n",
+ " y_df.merge(\n",
+ " sample_freeze, how='left', left_index=True, right_on='SAMPLE_BARCODE'\n",
+ " )\n",
+ " .set_index('SAMPLE_BARCODE')\n",
+ " .merge(mutation_burden, left_index=True, right_index=True)\n",
+ " )\n",
+ " disease_counts_df = pd.DataFrame(y_df.groupby('DISEASE').sum()['status'])\n",
+ " disease_proportion_df = disease_counts_df.divide(\n",
+ " y_df['DISEASE'].value_counts(sort=False).sort_index(), axis=0\n",
+ " )\n",
+ " filter_disease_df = (disease_counts_df > cfg.filter_count) & (disease_proportion_df > cfg.filter_prop)\n",
+ " disease_proportion_df['disease_included'] = filter_disease_df\n",
+ " disease_proportion_df['count'] = disease_counts_df['status']\n",
+ " filter_disease_df.columns = ['disease_included']\n",
+ " return filter_disease_df, disease_proportion_df\n",
+ "\n",
+ "\n",
+ "def get_all_valid_combos():\n",
+ " # not currently using this function, takes a while (5-10 minutes) to run\n",
+ " valid_combos_df = pd.DataFrame()\n",
+ " counter = 0\n",
+ " for gene in overlap_genes:\n",
+ " filter_df, _ = filter_cancer_types(gene, mutation_df.loc[:, gene],\n",
+ " sample_freeze_df, mut_burden_df)\n",
+ " valid_df = (\n",
+ " filter_df.query('disease_included')\n",
+ " .drop(['disease_included'], axis='columns')\n",
+ " .reset_index()\n",
+ " .rename({'DISEASE': 'disease'}, axis='columns')\n",
+ " )\n",
+ " valid_df['gene'] = gene\n",
+ " if len(valid_df) > 0:\n",
+ " valid_combos_df = pd.concat((valid_combos_df, valid_df))\n",
+ " counter += 1\n",
+ " if counter % 500 == 0:\n",
+ " print('{} done'.format(counter), file=sys.stderr)\n",
+ " print('done.', file=sys.stderr)\n",
+ " return valid_combos_df\n",
+ "\n",
+ "def get_top_valid_combos(top_genes_df):\n",
+ " top_genes_combos_df = pd.DataFrame()\n",
+ " for gene in top_genes_df['gene']:\n",
+ " _, status_df = filter_cancer_types(gene, mutation_df.loc[:, gene],\n",
+ " sample_freeze_df, mut_burden_df)\n",
+ " status_df = status_df.reset_index()\n",
+ " status_df['gene'] = gene\n",
+ " status_df.rename({'DISEASE': 'disease'}, axis='columns', inplace=True)\n",
+ " top_genes_combos_df = pd.concat((top_genes_combos_df, status_df))\n",
+ " return top_genes_combos_df \n",
+ "\n",
+ "top_genes_df = du.load_top_50()\n",
+ "filtered_combos_df = get_top_valid_combos(top_genes_df)\n",
+ "filtered_combos_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "536 combos out of 1650 possibilities (0.325%)\n",
+ "50 genes have valid combinations, out of 50 total\n",
+ "24 cancers have valid combinations, out of 33 total\n",
+ "['BLCA' 'BRCA' 'CESC' 'COAD' 'ESCA' 'GBM' 'HNSC' 'KICH' 'KIRC' 'KIRP'\n",
+ " 'LGG' 'LIHC' 'LUAD' 'LUSC' 'OV' 'PAAD' 'PRAD' 'READ' 'SARC' 'SKCM' 'STAD'\n",
+ " 'THCA' 'UCEC' 'UCS']\n",
+ "{'UVM', 'TGCT', 'DLBC', 'ACC', 'MESO', 'PCPG', 'CHOL', 'THYM', 'LAML'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "top_valid_df = (\n",
+ " filtered_combos_df[filtered_combos_df.disease_included].drop(['disease_included'], axis='columns')\n",
+ ")\n",
+ "print(len(top_valid_df), 'combos out of', 50 *33, 'possibilities ({:.3f}%)'.format(len(top_valid_df) / (50 * 33)))\n",
+ "unique_genes = np.unique(top_valid_df.gene)\n",
+ "print(len(unique_genes), 'genes have valid combinations, out of', top_genes_df.shape[0], 'total')\n",
+ "unique_cancers = np.unique(top_valid_df.disease)\n",
+ "all_cancers = cancer_types_df['cancertype'].values\n",
+ "print(len(unique_cancers), 'cancers have valid combinations, out of', len(all_cancers), 'total')\n",
+ "print(unique_cancers)\n",
+ "print(set(all_cancers) - set(unique_cancers))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we plot the results, with different colors for diseases that are included and removed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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