diff --git a/scripts/abc_alternative_genes_pathwaymapper.ipynb b/scripts/abc_alternative_genes_pathwaymapper.ipynb new file mode 100644 index 0000000..d2a0884 --- /dev/null +++ b/scripts/abc_alternative_genes_pathwaymapper.ipynb @@ -0,0 +1,1624 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/data/vijay/git/pancancer/scripts/util', '/data/vijay/git/pancancer/scripts/util', '', '/home/nagampv/anaconda3/lib/python36.zip', '/home/nagampv/anaconda3/lib/python3.6', '/home/nagampv/anaconda3/lib/python3.6/lib-dynload', '/home/nagampv/anaconda3/lib/python3.6/site-packages', '/home/nagampv/anaconda3/lib/python3.6/site-packages/IPython/extensions', '/home/nagampv/.ipython']\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import argparse\n", + "from sklearn.metrics import roc_auc_score, average_precision_score\n", + "sys.path.insert(0,'/data/vijay/git/pancancer/scripts/util')\n", + "print(sys.path)\n", + "#from data.vijay.git.pancancer.scripts.util.tcga_util import get_args\n", + "from tcga_util import get_args" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/data/vijay/git/pancancer/scripts\n" + ] + } + ], + "source": [ + "# Get the current working directory\n", + "cwd = os.getcwd()\n", + "print(cwd)\n", + "# Ensure that the path is starting in the scripts directory\n", + "if not cwd.split('/')[-1] == 'scripts':\n", + " sys.path.append(os.path.join(cwd, 'scripts'))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def get_gene_auroc(x, w):\n", + " score = roc_auc_score(x, w, average='weighted')\n", + " return(score)\n", + "\n", + "def get_gene_auprc(x, w):\n", + " score = average_precision_score(x, w, average='weighted')\n", + " return(score)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#args = get_args()\n", + "#alt_folder = args.alt_folder\n", + "#abc_folder = alt_folder\n", + "\n", + "abc_folder = os.path.join('..', 'classifiers', 'PI3K_GAIN_MOD')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Datasets\n", + "mut_file = os.path.join('..', 'data', 'pancan_mutation_freeze.tsv')\n", + "sample_freeze_file = os.path.join('..', 'data', 'sample_freeze.tsv')\n", + "copy_loss_file = os.path.join('..', 'data', 'copy_number_loss_status.tsv')\n", + "copy_gain_file = os.path.join('..', 'data', 'copy_number_gain_status.tsv')\n", + "\n", + "mutation_df = pd.read_table(mut_file, index_col=0)\n", + "sample_freeze = pd.read_table(sample_freeze_file, index_col=0)\n", + "copy_loss_df = pd.read_table(copy_loss_file, index_col=0)\n", + "copy_gain_df = pd.read_table(copy_gain_file, index_col=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 24 columns

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PIK3CAPIK3CBPIK3R2AKT1AKT2AKT3RHEBRPTORRICTORMTOR...TSC1TSC2NF1SAMPLE_BARCODEDISEASEweighttotal_statuslog10_muthypermutatedinclude
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TCGA-02-2483-010000000000...000TCGA-02-2483-01GBM0.43827801.66275800
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5 rows × 31 columns

\n", + "
" + ], + "text/plain": [ + " PIK3CA PIK3CB PIK3R2 AKT1 AKT2 AKT3 RHEB RPTOR \\\n", + "SAMPLE_BARCODE \n", + "TCGA-02-0047-01 1 0 0 0 0 0 0 0 \n", + "TCGA-02-0055-01 0 0 0 0 0 0 0 0 \n", + "TCGA-02-2483-01 0 0 0 0 0 0 0 0 \n", + "TCGA-02-2485-01 1 0 0 0 0 1 0 0 \n", + "TCGA-02-2486-01 0 0 0 0 0 0 0 0 \n", + "\n", + " RICTOR MTOR ... TSC1 TSC2 NF1 SAMPLE_BARCODE \\\n", + "SAMPLE_BARCODE ... \n", + "TCGA-02-0047-01 0 0 ... 0 0 0 TCGA-02-0047-01 \n", + "TCGA-02-0055-01 0 0 ... 0 0 1 TCGA-02-0055-01 \n", + "TCGA-02-2483-01 0 0 ... 0 0 0 TCGA-02-2483-01 \n", + "TCGA-02-2485-01 0 0 ... 0 0 0 TCGA-02-2485-01 \n", + "TCGA-02-2486-01 0 0 ... 0 0 0 TCGA-02-2486-01 \n", + "\n", + " DISEASE weight total_status log10_mut hypermutated \\\n", + "SAMPLE_BARCODE \n", + "TCGA-02-0047-01 GBM 0.442756 1 1.812913 0 \n", + "TCGA-02-0055-01 GBM 0.241695 0 1.707570 0 \n", + "TCGA-02-2483-01 GBM 0.438278 0 1.662758 0 \n", + "TCGA-02-2485-01 GBM 0.480901 1 1.748188 0 \n", + "TCGA-02-2486-01 GBM 0.388514 0 1.755875 0 \n", + "\n", + " include \n", + "SAMPLE_BARCODE \n", + "TCGA-02-0047-01 0 \n", + "TCGA-02-0055-01 0 \n", + "TCGA-02-2483-01 0 \n", + "TCGA-02-2485-01 0 \n", + "TCGA-02-2486-01 0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset_columns = ['SAMPLE_BARCODE', 'DISEASE', 'weight', 'total_status', 'log10_mut',\n", + " 'hypermutated', 'include']\n", + "pi3k_decisions_subset_df = pi3k_decisions_df[subset_columns]\n", + "pi3k_full_status_df = pi3k_status_df.merge(pi3k_decisions_subset_df, left_index=True,\n", + " right_on='SAMPLE_BARCODE')\n", + "pi3k_full_status_df.index = pi3k_full_status_df['SAMPLE_BARCODE']\n", + "pi3k_full_status_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9074, 31)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove hyper mutated samples\n", + "burden_filter = pi3k_full_status_df['hypermutated'] == 0\n", + "burden_filter = burden_filter & pi3k_full_status_df['log10_mut'] < 5 * pi3k_full_status_df['log10_mut'].std()\n", + "pi3k_full_status_df = pi3k_full_status_df[burden_filter]\n", + "pi3k_full_status_df.head(3)\n", + "pi3k_full_status_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "full_auroc = (\n", + " pi3k_full_status_df[pi3k_genes_df['genes']]\n", + " .apply(lambda x: get_gene_auroc(x, pi3k_full_status_df['weight']))\n", + " )\n", + "full_auprc = (\n", + " pi3k_full_status_df[pi3k_genes_df['genes']]\n", + " .apply(lambda x: get_gene_auprc(x, pi3k_full_status_df['weight']))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['AKT1', 'PIK3CA', 'PIK3R1', 'PTEN', 'STK11', 'INPP4B']\n" + ] + }, + { + "data": { + "text/plain": [ + "(6273, 18)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove pi3k positive samples, and recalculate metrics\n", + "#drop abc genes:\n", + "genes_df = pd.read_csv(\"/data/vijay/git/pancancer/data/abc_genes.csv\")\n", + "genes = genes_df['genes'].tolist()\n", + "print(genes)\n", + "remove_pi3k_status = pi3k_full_status_df[pi3k_full_status_df['total_status'] == 0]\n", + "remove_pi3k_status_df = remove_pi3k_status[pi3k_genes_df['genes']]\n", + "remove_pi3k_status_df = remove_pi3k_status_df.drop(genes, axis=1)\n", + "remove_pi3k_status_df.shape\n", + "#full_auroc_remove = remove_pi3k_status_df.apply(lambda x: get_gene_auroc(x, w=remove_pi3k_status['weight']))\n", + "#full_auprc_remove = remove_pi3k_status_df.apply(lambda x: get_gene_auprc(x, w=remove_pi3k_status['weight']))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pi3k_aurocno_pi3k_aurocpi3k_auprcno_pi3k_auprc
PIK3CA11.53819311.53819329.70770929.707709
PIK3CB13.2920596.6435296.0492013.219805
PIK3R27.0916263.3963802.9222852.030019
AKT111.17099311.1709933.0119513.011951
AKT28.1588486.6186883.2950872.799736
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" + ], + "text/plain": [ + " pi3k_auroc no_pi3k_auroc pi3k_auprc no_pi3k_auprc\n", + "PIK3CA 11.538193 11.538193 29.707709 29.707709\n", + "PIK3CB 13.292059 6.643529 6.049201 3.219805\n", + "PIK3R2 7.091626 3.396380 2.922285 2.030019\n", + "AKT1 11.170993 11.170993 3.011951 3.011951\n", + "AKT2 8.158848 6.618688 3.295087 2.799736" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get output metrics for pi3k classification\n", + "output_pi3k_metrics = pd.concat([full_auroc, full_auroc_remove], axis=1, sort=False)\n", + "output_pi3k_metrics = output_pi3k_metrics * 100 # To get percent\n", + "output_pi3k_metrics = output_pi3k_metrics - 50 # Subtract 50 from AUROC only\n", + "\n", + "# Combine with AUPRC\n", + "output_pi3k_metrics = pd.concat([output_pi3k_metrics, full_auprc * 100,\n", + " full_auprc_remove * 100], axis=1, sort=False)\n", + "output_pi3k_metrics.columns = ['pi3k_auroc', 'no_pi3k_auroc', 'pi3k_auprc', 'no_pi3k_auprc']\n", + "\n", + "# Fill removed pi3k metrics with included metrics\n", + "output_pi3k_metrics['no_pi3k_auroc'] = (\n", + " output_pi3k_metrics['no_pi3k_auroc'].fillna(output_pi3k_metrics['pi3k_auroc'])\n", + " )\n", + "output_pi3k_metrics['no_pi3k_auprc'] = (\n", + " output_pi3k_metrics['no_pi3k_auprc'].fillna(output_pi3k_metrics['pi3k_auprc'])\n", + " )\n", + "\n", + "# Write results to file\n", + "tables_folder = os.path.join(abc_folder, 'tables')\n", + "\n", + "if not os.path.exists(tables_folder):\n", + " os.makedirs(tables_folder)\n", + "\n", + "pi3k_metric_file = os.path.join(abc_folder, 'tables', 'pi3k_metrics_pathwaymapper.txt')\n", + "output_pi3k_metrics.to_csv(pi3k_metric_file, sep='\\t')\n", + "\n", + "output_pi3k_metrics.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pi3k Pathway Performance Summary: All pi3k Genes\n", + "AUROC:\n", + "0.6382545864112827\n", + "AUPRC:\n", + "0.7275336003463387\n" + ] + } + ], + "source": [ + "# Display pi3k pathway metrics\n", + "all_samples_pi3k_pathway_status = pi3k_full_status_df[pi3k_genes_df['genes']].max(axis=1)\n", + "print('pi3k Pathway Performance Summary: All pi3k Genes')\n", + "print('AUROC:')\n", + "print(roc_auc_score(all_samples_pi3k_pathway_status,\n", + " pi3k_full_status_df['weight'], average='weighted'))\n", + "print('AUPRC:')\n", + "print(average_precision_score(all_samples_pi3k_pathway_status,\n", + " pi3k_full_status_df['weight'], average='weighted'))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pi3k Pathway Performance Summary: ['AKT1', 'PIK3CA', 'PIK3R1', 'PTEN', 'STK11', 'INPP4B']\n", + "AUROC:\n", + "0.8199567751744343\n", + "AUPRC:\n", + "0.6233651115489887\n" + ] + } + ], + "source": [ + "print('pi3k Pathway Performance Summary:', genes)\n", + "print('AUROC:')\n", + "print(roc_auc_score(pi3k_full_status_df['total_status'],\n", + " pi3k_full_status_df['weight'], average='weighted'))\n", + "print('AUPRC:')\n", + "print(average_precision_score(pi3k_full_status_df['total_status'],\n", + " pi3k_full_status_df['weight'], average='weighted'))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pi3k Pathway Performance Summary: Held Out Samples\n", + "AUROC:\n", + "0.7051315768627513\n", + "AUPRC:\n", + "0.37883676768463836\n" + ] + } + ], + "source": [ + "print('pi3k Pathway Performance Summary: Held Out Samples')\n", + "held_out_pi3k_df = pi3k_full_status_df[pi3k_full_status_df['include'] == 0]\n", + "print('AUROC:')\n", + "print(roc_auc_score(held_out_pi3k_df['total_status'],\n", + " held_out_pi3k_df['weight'], average='weighted'))\n", + "print('AUPRC:')\n", + "print(average_precision_score(held_out_pi3k_df['total_status'],\n", + " held_out_pi3k_df['weight'], average='weighted'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualize Distribution of AUROC and AUPRC for all genes" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TP53TTNMUC16PIK3CACSMD3RYR2LRP1BSYNE1FLGUSH2A...C17orf50C17orf105C17orf103MBD3L2DKFZP761K2322RP11-434D2.3FXYD4AC110619.2RP11-386M24.4C15orf40
SAMPLE_BARCODE
TCGA-02-0047-010001010000...0000000000
TCGA-02-0055-011100000000...0000000000
TCGA-02-2483-011000000000...0000000000
TCGA-02-2485-011011000010...0000000000
TCGA-02-2486-010000100000...0000000000
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5 rows × 19372 columns

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" + ], + "text/plain": [ + " TP53 TTN MUC16 PIK3CA CSMD3 RYR2 LRP1B SYNE1 FLG \\\n", + "SAMPLE_BARCODE \n", + "TCGA-02-0047-01 0 0 0 1 0 1 0 0 0 \n", + "TCGA-02-0055-01 1 1 0 0 0 0 0 0 0 \n", + "TCGA-02-2483-01 1 0 0 0 0 0 0 0 0 \n", + "TCGA-02-2485-01 1 0 1 1 0 0 0 0 1 \n", + "TCGA-02-2486-01 0 0 0 0 1 0 0 0 0 \n", + "\n", + " USH2A ... C17orf50 C17orf105 C17orf103 MBD3L2 \\\n", + "SAMPLE_BARCODE ... \n", + "TCGA-02-0047-01 0 ... 0 0 0 0 \n", + "TCGA-02-0055-01 0 ... 0 0 0 0 \n", + "TCGA-02-2483-01 0 ... 0 0 0 0 \n", + "TCGA-02-2485-01 0 ... 0 0 0 0 \n", + "TCGA-02-2486-01 0 ... 0 0 0 0 \n", + "\n", + " DKFZP761K2322 RP11-434D2.3 FXYD4 AC110619.2 \\\n", + "SAMPLE_BARCODE \n", + "TCGA-02-0047-01 0 0 0 0 \n", + "TCGA-02-0055-01 0 0 0 0 \n", + "TCGA-02-2483-01 0 0 0 0 \n", + "TCGA-02-2485-01 0 0 0 0 \n", + "TCGA-02-2486-01 0 0 0 0 \n", + "\n", + " RP11-386M24.4 C15orf40 \n", + "SAMPLE_BARCODE \n", + "TCGA-02-0047-01 0 0 \n", + "TCGA-02-0055-01 0 0 \n", + "TCGA-02-2483-01 0 0 \n", + "TCGA-02-2485-01 0 0 \n", + "TCGA-02-2486-01 0 0 \n", + "\n", + "[5 rows x 19372 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Subset mutation file by samples\n", + "sub_full_mutation_df = mutation_df[burden_filter]\n", + "low_mutation_count_filter = (\n", + " sub_full_mutation_df.sum()\n", + " [sub_full_mutation_df.sum() >= 10].sort_values(ascending=False).index\n", + " )\n", + "sub_full_mutation_df = sub_full_mutation_df[low_mutation_count_filter]\n", + "sub_full_mutation_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Get Metrics for All Genes\n", + "all_auprc = sub_full_mutation_df.apply(lambda x: get_gene_auprc(x, w = pi3k_full_status_df['weight']))\n", + "all_auroc = sub_full_mutation_df.apply(lambda x: get_gene_auroc(x, w = pi3k_full_status_df['weight']))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GeneAUPRCAUPRC Rankpi3kAUROCAUROC Rank
0PTEN0.588693010.87127574
1TP530.368805100.50937318960
2TTN0.354309200.55726717178
3PIK3CA0.263492310.6308816932
4ARID1A0.252372400.6649793117
5PIK3R10.227745510.770791343
6MUC160.222872600.53010018543
7RYR20.171702700.58096214726
8CTCF0.171120800.733214645
9CSMD30.170909900.56505816510
\n", + "
" + ], + "text/plain": [ + " Gene AUPRC AUPRC Rank pi3k AUROC AUROC Rank\n", + "0 PTEN 0.588693 0 1 0.871275 74\n", + "1 TP53 0.368805 1 0 0.509373 18960\n", + "2 TTN 0.354309 2 0 0.557267 17178\n", + "3 PIK3CA 0.263492 3 1 0.630881 6932\n", + "4 ARID1A 0.252372 4 0 0.664979 3117\n", + "5 PIK3R1 0.227745 5 1 0.770791 343\n", + "6 MUC16 0.222872 6 0 0.530100 18543\n", + "7 RYR2 0.171702 7 0 0.580962 14726\n", + "8 CTCF 0.171120 8 0 0.733214 645\n", + "9 CSMD3 0.170909 9 0 0.565058 16510" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Process file and save results\n", + "all_gene_metrics_file = os.path.join(abc_folder, 'tables', 'all_gene_metric_ranks.tsv')\n", + "\n", + "all_genes_auprc_df = pd.DataFrame(all_auprc.sort_values(ascending=False), columns=['auprc'])\n", + "all_genes_auroc_df = pd.DataFrame(all_auroc.sort_values(ascending=False), columns=['auroc'])\n", + "\n", + "all_genes_auprc_df = all_genes_auprc_df.assign(auprc_rank = list(range(0, all_genes_auprc_df.shape[0])))\n", + "all_genes_auroc_df = all_genes_auroc_df.assign(auroc_rank = list(range(0, all_genes_auprc_df.shape[0])))\n", + "\n", + "all_genes_auprc_df = all_genes_auprc_df.assign(pi3k = 0)\n", + "all_genes_auprc_df.loc[all_genes_auprc_df.index.isin(pi3k_genes_df['genes']), 'pi3k'] = 1\n", + "\n", + "all_genes_metrics_df = all_genes_auprc_df.reset_index().merge(all_genes_auroc_df,\n", + " left_on='index', right_index=True)\n", + "\n", + "all_genes_metrics_df.columns = ['Gene', 'AUPRC', 'AUPRC Rank', 'pi3k', 'AUROC', 'AUROC Rank']\n", + "all_genes_metrics_df.to_csv(all_gene_metrics_file, sep='\\t', index=False)\n", + "all_genes_metrics_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/scripts/abc_alternative_genes_pathwaymapper.py b/scripts/abc_alternative_genes_pathwaymapper.py new file mode 100644 index 0000000..babdada --- /dev/null +++ b/scripts/abc_alternative_genes_pathwaymapper.py @@ -0,0 +1,276 @@ + +# coding: utf-8 + +# In[1]: + + +import os +import sys +import pandas as pd + +import matplotlib.pyplot as plt +import seaborn as sns +import argparse +from sklearn.metrics import roc_auc_score, average_precision_score +sys.path.insert(0, os.path.join('scripts', 'util')) +from tcga_util import get_args + + +# In[2]: + + +# Get the current working directory +cwd = os.getcwd() + +# Ensure that the path is starting in the scripts directory +if not cwd.split('/')[-1] == 'scripts': + sys.path.append(os.path.join(cwd, 'scripts')) + + +# In[3]: + + +def get_gene_auroc(x, w): + score = roc_auc_score(x, w, average='weighted') + return(score) + +def get_gene_auprc(x, w): + score = average_precision_score(x, w, average='weighted') + return(score) + + +# In[4]: + +# setting abc directory [abc_dir='classifiers/PI3K'] + +args = get_args() +alt_folder = args.alt_folder +if alt_folder != 'Auto': + abc_folder = alt_folder + +#abc_folder = os.path.join('..', 'classifiers', 'PI3K_LOSS') + + +# In[5]: + + +# Load Datasets + +#mut_file = os.path.join('..', 'data', 'pancan_mutation_freeze.tsv') +#sample_freeze_file = os.path.join('..', 'data', 'sample_freeze.tsv') +#copy_loss_file = os.path.join('..', 'data', 'copy_number_loss_status.tsv') +#copy_gain_file = os.path.join('..', 'data', 'copy_number_gain_status.tsv') + +mut_file = os.path.join('data', 'pancan_mutation_freeze.tsv') +sample_freeze_file = os.path.join('data', 'sample_freeze.tsv') +copy_loss_file = os.path.join('data', 'copy_number_loss_status.tsv') +copy_gain_file = os.path.join('data', 'copy_number_gain_status.tsv') + + +mutation_df = pd.read_table(mut_file, index_col=0) +sample_freeze = pd.read_table(sample_freeze_file, index_col=0) +copy_loss_df = pd.read_table(copy_loss_file, index_col=0) +copy_gain_df = pd.read_table(copy_gain_file, index_col=0) + + +# In[6]: + + +# Load pi3k Pathway Genes +pathway_genes_file = os.path.join('data', 'pi3k_genes.csv') +pathway_genes_df = pd.read_csv(pathway_genes_file) + +# In[7]: + + +# Load classifier weights +abc_decision_file = os.path.join(abc_folder, 'classifier_decisions.tsv') +abc_decisions_df = pd.read_table(abc_decision_file) + +# In[8]: + + +pathway_mutations_df = mutation_df[pathway_genes_df['genes']] + +# Add status to the Y matrix depending on if the gene is a tumor suppressor +# or an oncogene. An oncogene can be activated with copy number gains, but +# a tumor suppressor is inactivated with copy number loss + +oncogene = pathway_genes_df[pathway_genes_df['og_tsg'] == 'OG'] +tumor_suppressor = pathway_genes_df[pathway_genes_df['og_tsg'] == 'TSG'] + +# Subset copy number information +pathway_copy_gain_sub_df = copy_gain_df[oncogene['genes']] +pathway_copy_loss_sub_df = copy_loss_df[tumor_suppressor['genes']] + +# Combine Copy Number data +pathway_copy_df = pd.concat([pathway_copy_gain_sub_df, pathway_copy_loss_sub_df], axis=1) + + +# In[9]: + +pathway_status_df = pathway_mutations_df + pathway_copy_df +pathway_status_df[pathway_status_df == 2] = 1 + + +# In[10]: + + +subset_columns = ['SAMPLE_BARCODE', 'DISEASE', 'weight', 'total_status', 'log10_mut', + 'hypermutated', 'include'] +abc_decisions_subset_df = abc_decisions_df[subset_columns] +pathway_full_status_df = pathway_status_df.merge(abc_decisions_subset_df, left_index=True, + right_on='SAMPLE_BARCODE') +pathway_full_status_df.index = pathway_full_status_df['SAMPLE_BARCODE'] + + +# In[11]: + + +# Remove hyper mutated samples +burden_filter = pathway_full_status_df['hypermutated'] == 0 +burden_filter = burden_filter & pathway_full_status_df['log10_mut'] < 5 * pathway_full_status_df['log10_mut'].std() +pathway_full_status_df = pathway_full_status_df[burden_filter] + +# In[12]: + + +full_auroc = ( + pathway_full_status_df[pathway_genes_df['genes']] + .apply(lambda x: get_gene_auroc(x, pathway_full_status_df['weight'])) + ) + +full_auprc = ( + pathway_full_status_df[pathway_genes_df['genes']] + .apply(lambda x: get_gene_auprc(x, pathway_full_status_df['weight'])) + ) + + +# In[13]: + + +# Remove pi3k positive samples, and recalculate metrics +#drop abc genes: +genes_df = pd.read_csv("/data/vijay/git/pancancer/data/abc_genes.csv") +genes = genes_df['genes'].tolist() +remove_abc_status = pathway_full_status_df[pathway_full_status_df['total_status'] == 0] +remove_abc_status_df = remove_abc_status[pathway_genes_df['genes']] +remove_abc_status_df = remove_abc_status_df.drop(genes, axis=1) +full_auroc_remove = remove_abc_status_df.apply(lambda x: get_gene_auroc(x, w=remove_abc_status['weight'])) +full_auprc_remove = remove_abc_status_df.apply(lambda x: get_gene_auprc(x, w=remove_abc_status['weight'])) + + +# In[16]: + + +# Get output metrics for pi3k classification +output_pathway_metrics = pd.concat([full_auroc, full_auroc_remove], axis=1, sort=False) +output_pathway_metrics = output_pathway_metrics * 100 # To get percent +output_pathway_metrics = output_pathway_metrics - 50 # Subtract 50 from AUROC only + +# Combine with AUPRC +output_pathway_metrics = pd.concat([output_pathway_metrics, full_auprc * 100, + full_auprc_remove * 100], axis=1, sort=False) +output_pathway_metrics.columns = ['pathway_auroc', 'no_abc_auroc', 'pathway_auprc', 'no_abc_auprc'] + +# Fill removed pi3k metrics with included metrics +output_pathway_metrics['no_abc_auroc'] = ( + output_pathway_metrics['no_abc_auroc'].fillna(output_pathway_metrics['pathway_auroc']) + ) +output_pathway_metrics['no_abc_auprc'] = ( + output_pathway_metrics['no_abc_auprc'].fillna(output_pathway_metrics['pathway_auprc']) + ) + +# Write results to file +tables_folder = os.path.join(abc_folder, 'tables') + +if not os.path.exists(tables_folder): + os.makedirs(tables_folder) + +pathway_metric_file = os.path.join(abc_folder, 'tables', 'pathway_metrics_pathwaymapper.txt') +output_pathway_metrics.to_csv(pathway_metric_file, sep='\t') + + +# In[17]: + + +# Display pi3k pathway metrics +all_samples_abc_pathway_status = pathway_full_status_df[pathway_genes_df['genes']].max(axis=1) +print('abc Pathway Performance Summary: All pathway Genes') +print('AUROC:') +print(roc_auc_score(all_samples_abc_pathway_status, + pathway_full_status_df['weight'], average='weighted')) +print('AUPRC:') +print(average_precision_score(all_samples_abc_pathway_status, + pathway_full_status_df['weight'], average='weighted')) + + +# In[18]: + +print('abc Pathway Performance Summary:', genes) +print('AUROC:') +print(roc_auc_score(pathway_full_status_df['total_status'], + pathway_full_status_df['weight'], average='weighted')) +print('AUPRC:') +print(average_precision_score(pathway_full_status_df['total_status'], + pathway_full_status_df['weight'], average='weighted')) + + +# In[19]: + +print('abc Pathway Performance Summary: Held Out Samples') +held_out_pathway_df = pathway_full_status_df[pathway_full_status_df['include'] == 0] +print('AUROC:') +print(roc_auc_score(held_out_pathway_df['total_status'], + held_out_pathway_df['weight'], average='weighted')) +print('AUPRC:') +print(average_precision_score(held_out_pathway_df['total_status'], + held_out_pathway_df['weight'], average='weighted')) + + +# # Visualize Distribution of AUROC and AUPRC for all genes + +# In[20]: + + +# Subset mutation file by samples +sub_full_mutation_df = mutation_df[burden_filter] +low_mutation_count_filter = ( + sub_full_mutation_df.sum() + [sub_full_mutation_df.sum() >= 10].sort_values(ascending=False).index + ) +sub_full_mutation_df = sub_full_mutation_df[low_mutation_count_filter] +sub_full_mutation_df.head() + + +# In[21]: + + +# Get Metrics for All Genes +all_auprc = sub_full_mutation_df.apply(lambda x: get_gene_auprc(x, w = pathway_full_status_df['weight'])) +all_auroc = sub_full_mutation_df.apply(lambda x: get_gene_auroc(x, w = pathway_full_status_df['weight'])) + + +# In[23]: + + +# Process file and save results +all_gene_metrics_file = os.path.join(abc_folder, 'tables', 'all_gene_metric_ranks.tsv') + +all_genes_auprc_df = pd.DataFrame(all_auprc.sort_values(ascending=False), columns=['auprc']) +all_genes_auroc_df = pd.DataFrame(all_auroc.sort_values(ascending=False), columns=['auroc']) + +all_genes_auprc_df = all_genes_auprc_df.assign(auprc_rank = list(range(0, all_genes_auprc_df.shape[0]))) +all_genes_auroc_df = all_genes_auroc_df.assign(auroc_rank = list(range(0, all_genes_auprc_df.shape[0]))) + +all_genes_auprc_df = all_genes_auprc_df.assign(abc = 0) +all_genes_auprc_df.loc[all_genes_auprc_df.index.isin(pathway_genes_df['genes']), 'abc'] = 1 + +all_genes_metrics_df = all_genes_auprc_df.reset_index().merge(all_genes_auroc_df, + left_on='index', right_index=True) + +all_genes_metrics_df.columns = ['Gene', 'AUPRC', 'AUPRC Rank', 'abc', 'AUROC', 'AUROC Rank'] +all_genes_metrics_df.to_csv(all_gene_metrics_file, sep='\t', index=False) + +print("all_gene_metric_ranks file generated") +print("abc_alternative_genes_pathwaymapper done") \ No newline at end of file diff --git a/scripts/abc_pathway_count_heatmaps.py b/scripts/abc_pathway_count_heatmaps.py new file mode 100644 index 0000000..bf0ac0a --- /dev/null +++ b/scripts/abc_pathway_count_heatmaps.py @@ -0,0 +1,323 @@ + +# coding: utf-8 + +# In[1]: + + +import os +import sys +import pandas as pd +import argparse +import matplotlib.pyplot as plt +import seaborn as sns + +parser = argparse.ArgumentParser() + +#parser.add_argument('-c', '--classifier', default=None, +# help='folder location of classifier file') +#parser.add_argument('-u', '--copy_number', action='store_true', +# help='Supplement Y matrix with copy number events') +parser.add_argument('-g', '--genes', + help='gene list file') +parser.add_argument('-p', '--path_genes', + help='pathway gene list file') +parser.add_argument('-o', '--alt_folder', default='Auto', + help='Provide an alternative folder to save results') +parser.add_argument('-x', '--x_matrix', default=None, + help='Filename of features to use in model') +parser.add_argument( '--filename_mut', default=None, + help='Filename of sample/gene mutations to use in model') +parser.add_argument( '--filename_mut_burden', default=None, + help='Filename of sample mutation burden to use in model') +parser.add_argument( '--filename_sample', default=None, + help='Filename of patient/samples to use in model') +parser.add_argument( '--filename_copy_loss', default=None, + help='Filename of copy number loss') +parser.add_argument( '--filename_copy_gain', default=None, + help='Filename of copy number gain') +parser.add_argument( '--filename_cancer_gene_classification', default=None, + help='Filename of cancer gene classification table') + +args = parser.parse_args() + +# Load command arguments +#classifier_file = os.path.join(args.classifier, 'classifier_summary.txt') +#copy_number = args.copy_number + +# Load Constants +#rnaseq_file = args.x_matrix or os.path.join('data', 'pancan_rnaseq_freeze.tsv') +#mut_file = args.filename_mut or os.path.join('data', 'pancan_mutation_freeze.tsv') +#sample_freeze_file = args.filename_sample or os.path.join('data', 'sample_freeze.tsv') +#cancer_gene_file = args.filename_cancer_gene_classification or os.path.join('data', 'vogelstein_cancergenes.tsv') +#copy_loss_file = args.filename_copy_loss or os.path.join('data', 'copy_number_loss_status.tsv') +#copy_gain_file = args.filename_copy_gain or os.path.join('data', 'copy_number_gain_status.tsv') +#mutation_burden_file = args.filename_mut_burden or os.path.join('data', 'mutation_burden_freeze.tsv') + +alt_folder = args.alt_folder +rnaseq_file = args.x_matrix +mut_file = args.filename_mut +sample_freeze_file = args.filename_sample +cancer_gene_file = args.filename_cancer_gene_classification +copy_loss_file = args.filename_copy_loss +copy_gain_file = args.filename_copy_gain +mutation_burden_file = args.filename_mut_burden + +# In[2]: + +# Get the current working directory +#cwd = os.getcwd() + +# Ensure that the path is starting in the scripts directory +#if not cwd.split('/')[-1] == 'scripts': +# os.chdir(os.path.join(cwd, 'scripts')) + +# In[3]: + +#get_ipython().run_line_magic('matplotlib', 'inline') +#plt.style.use('seaborn-notebook') + +# In[4]: + +# Load Datasets +#mut_file = os.path.join('..', 'data', 'pancan_mutation_freeze.tsv.gz') +#sample_freeze_file = os.path.join('..', 'data', 'sample_freeze.tsv') +#copy_loss_file = os.path.join('..', 'data', 'copy_number_loss_status.tsv.gz') +#copy_gain_file = os.path.join('..', 'data', 'copy_number_gain_status.tsv.gz') +#cancer_genes_file = os.path.join('..', 'data', 'vogelstein_cancergenes.tsv') + +mutation_df = pd.read_table(mut_file, index_col=0) +mutation_df.head() +sample_freeze = pd.read_table(sample_freeze_file, index_col=0) +copy_loss_df = pd.read_table(copy_loss_file, index_col=0) +copy_gain_df = pd.read_table(copy_gain_file, index_col=0) +cancer_genes_df = pd.read_table(cancer_gene_file) + +# In[5]: + +# Load Ras Pathway Genes +#results_path = os.path.join('..', 'classifiers', 'RAS') +results_path = alt_folder + +# Load Ras Pathway Genes +#genes_file = os.path.join('..', 'data', 'ras_genes.csv') +genes_file = args.genes +genes_df = pd.read_table(genes_file) +genes = genes_df['genes'].tolist() + +pathgenes_file = args.path_genes +pathgenes_df = pd.read_csv(pathgenes_file) +print(pathgenes_df) + +n = pathgenes_df['og_tsg'].tolist() +n_OG = n.count('OG') +n_TSG = n.count('TSG') +print(n_OG) +print(n_TSG) + +# In[6]: + +genes_df.head() +pathgenes_df.head() + +# In[7]: + +# Subset mutation data +mutation_sub_df = mutation_df.loc[:, pathgenes_df['genes']] + +# In[8]: + +# Find if the input genes are in this master list +genes_sub = cancer_genes_df[cancer_genes_df['Gene Symbol'].isin(pathgenes_df['genes'])] + +# In[9]: + +# Add status to the Y matrix depending on if the gene is a tumor suppressor +# or an oncogene. An oncogene can be activated with copy number gains, but +# a tumor suppressor is inactivated with copy number loss +tumor_suppressor = pathgenes_df[pathgenes_df['og_tsg'] == 'TSG'] +oncogene = pathgenes_df[pathgenes_df['og_tsg'] == 'OG'] + +# Subset copy number information +copy_loss_sub_df = copy_loss_df[tumor_suppressor['genes']] +copy_gain_sub_df = copy_gain_df[oncogene['genes']] + +# ## Output Mutation, Copy Number, and Total Heatmap (Gene by Cancer-type) + +# In[10]: + +mutation_sub_total_df = mutation_sub_df.assign(Total=mutation_sub_df.max(axis=1)) +mut_disease_df = mutation_sub_total_df.merge(sample_freeze, left_index=True, + right_on='SAMPLE_BARCODE') +mut_heatmap_df = mut_disease_df.groupby('DISEASE').mean() + +# In[11]: + +gene_avg = mut_disease_df.mean() +gene_avg.name = 'Total' + +# In[12]: + +mut_heatmap_df = mut_heatmap_df.append(gene_avg) + +# In[13]: + +sns.set_style("whitegrid") +plt.figure(figsize = (10,10),dpi= 300) +sns.heatmap(mut_heatmap_df, linewidths=0.2, linecolor='black', + cmap='Blues_r', square=True, cbar=True) +plt.autoscale(enable=True, axis ='x', tight = True) +plt.autoscale(enable=True, axis ='y', tight = True) +plt.ylabel('Cancer Types', fontsize=16) +plt.xlabel('Pathway Genes', fontsize=16) +plt.savefig(os.path.join(results_path, 'mut_df.pdf')) +plt.savefig(os.path.join(results_path, 'mut_df.jpeg')) + +# In[14]: + +copy_df = pd.concat([copy_gain_sub_df, copy_loss_sub_df], axis=1) +copy_total_df = copy_df.assign(Total=copy_df.max(axis=1)) +copy_disease_df = copy_total_df.merge(sample_freeze, left_index=True, + right_on='SAMPLE_BARCODE') +copy_heatmap_df = copy_disease_df.groupby('DISEASE').mean() + +# In[15]: + +copy_avg = copy_disease_df.mean() +copy_avg.name = 'Total' + +# In[16]: + +copy_heatmap_df = copy_heatmap_df.append(copy_avg) + +# In[17]: + +sns.set_style("whitegrid") +plt.figure(figsize = (10,10),dpi= 300) +sns.heatmap(copy_heatmap_df, linewidths=0.2, linecolor='black', + cmap='Blues_r', square=True) +plt.ylabel('Cancer Types', fontsize=16) +plt.xlabel('Pathway Genes', fontsize=16) +plt.autoscale(enable=True, axis ='x', tight = True) +plt.autoscale(enable=True, axis ='y', tight = True) +plt.savefig(os.path.join(results_path, 'copy_df.pdf')) +plt.savefig(os.path.join(results_path, 'copy_df.jpeg')) + +# In[18]: + +# Combined heatmap +comb_heat = mutation_sub_df + copy_df +comb_heat[comb_heat == 2] = 1 # Replace duplicates with just one + +# In[19]: + +comb_heat_df = comb_heat.merge(sample_freeze, left_index=True, right_on='SAMPLE_BARCODE') +comb_heat_total_df = comb_heat_df.assign(Total=comb_heat_df.max(axis=1)) +comb_heatmap_df = comb_heat_total_df.groupby('DISEASE').mean() + +# In[20]: + +comb_avg = comb_heat_total_df.mean() +comb_avg.name = 'Total' + +# In[21]: + +comb_heatmap_plot = comb_heatmap_df.append(comb_avg) + +# In[22]: + +sns.set_style("whitegrid") +plt.figure(figsize = (10,10),dpi= 300) +sns.heatmap(comb_heatmap_plot, linewidths=0.2, linecolor='black', + cmap='Blues_r', square=True) +plt.ylabel('Cancer Types', fontsize=16) +plt.xlabel('Pathway Genes', fontsize=16) +plt.autoscale(enable=True, axis ='x', tight = True) +plt.autoscale(enable=True, axis ='y', tight = True) +plt.tight_layout() +plt.savefig(os.path.join(results_path, 'combined_df.pdf')) +plt.savefig(os.path.join(results_path, 'combined_df.jpeg')) + +# ## Generating Pathway Mapper Text Files + +# In[23]: + +summary_score_df = ( + pd.DataFrame( + [mut_heatmap_df.loc['Total', :], copy_heatmap_df.loc['Total', :]] + ) + .transpose() +) +summary_score_df.columns = ['mutation', 'copy_number'] +summary_score_df = summary_score_df * 100 +summary_score_df = summary_score_df.round(decimals = 1) + +# In[24]: +# Create negative percentages for tumor suppressors in the Pathway +tum_sup_mult = pd.Series([1] * n_OG + [-1] * n_TSG + [1]) +tum_sup_mult.index = summary_score_df.index + +# In[25]: + +summary_score_df = summary_score_df.mul(tum_sup_mult, axis=0) +pathway_mapper_file = os.path.join(results_path, 'tables', + 'pathwaymapper_percentages.txt') +summary_score_df.to_csv(pathway_mapper_file, sep='\t') + +# ## Output number of Ras events per sample + +# In[26]: +decision_file = os.path.join(results_path, 'classifier_decisions.tsv') +decisions_df = pd.read_table(decision_file) +decisions_df.head() + +# In[27]: +#other_ras_df = mutation_sub_df.drop(['KRAS', 'HRAS', 'NRAS'], axis=1) +#other_ras_copy_df = copy_df.drop(['KRAS', 'HRAS', 'NRAS'], axis=1) +#other_ras_all_df = comb_heat_df.drop(['KRAS', 'HRAS', 'NRAS'], axis=1) + +other_genes_df = mutation_sub_df.drop(genes, axis=1) +other_genes_copy_df = copy_df.drop(genes, axis=1) +other_genes_all_df = comb_heat_df.drop(genes, axis=1) + +# In[28]: + +total_genes_mutations = pd.DataFrame(other_genes_df.sum(axis=1), columns=['mutation_count']) +total_genes_copy_events = pd.DataFrame(other_genes_copy_df.sum(axis=1), columns=['copy_count']) +total_genes_all = pd.DataFrame(other_genes_all_df.sum(axis=1), columns=['all_count']) +total_genes_all.index = comb_heat_df['SAMPLE_BARCODE'] + +# In[29]: + +# Define output summary of mutation, copy, and total counts per sample by Ras pathway +count_summary = ( + decisions_df[['SAMPLE_BARCODE', 'DISEASE', 'weight']] + .merge(total_genes_mutations, left_on='SAMPLE_BARCODE', right_index=True) + ) +hyper_samples = decisions_df[decisions_df['hypermutated'] == 1]['SAMPLE_BARCODE'] +count_summary.loc[count_summary['SAMPLE_BARCODE'].isin(hyper_samples), + 'mutation_count'] = 'hyper' +count_summary.head() + +# In[30]: + +count_summary['mutation_count'].value_counts() + +# In[31]: + +count_summary = total_genes_copy_events.merge(count_summary, left_index=True, + right_on='SAMPLE_BARCODE') +count_summary = total_genes_all.merge(count_summary, left_index=True, + right_on='SAMPLE_BARCODE') +count_summary = ( + decisions_df[['SAMPLE_BARCODE', 'total_status']] + .merge(count_summary, left_on='SAMPLE_BARCODE', right_on='SAMPLE_BARCODE') + ) +count_summary.head() + +# In[32]: + +count_summary_file = os.path.join(results_path, 'tables', + 'path_events_per_sample.tsv') +count_summary.to_csv(count_summary_file, sep='\t', index=False) + +print("abc_pathway_count_heatmaps_generated and done") \ No newline at end of file diff --git a/scripts/apply_weights.py b/scripts/apply_weights.py index b3bf3ef..8bc6de0 100644 --- a/scripts/apply_weights.py +++ b/scripts/apply_weights.py @@ -31,6 +31,22 @@ help='folder location of classifier file') parser.add_argument('-u', '--copy_number', action='store_true', help='Supplement Y matrix with copy number events') + +parser.add_argument('-x', '--x_matrix', default=None, + help='Filename of features to use in model') +parser.add_argument( '--filename_mut', default=None, + help='Filename of sample/gene mutations to use in model') +parser.add_argument( '--filename_mut_burden', default=None, + help='Filename of sample mutation burden to use in model') +parser.add_argument( '--filename_sample', default=None, + help='Filename of patient/samples to use in model') +parser.add_argument( '--filename_copy_loss', default=None, + help='Filename of copy number loss') +parser.add_argument( '--filename_copy_gain', default=None, + help='Filename of copy number gain') +parser.add_argument( '--filename_cancer_gene_classification', default=None, + help='Filename of cancer gene classification table') + args = parser.parse_args() # Load command arguments @@ -38,13 +54,21 @@ copy_number = args.copy_number # Load Constants -rnaseq_file = os.path.join('data', 'pancan_rnaseq_freeze.tsv') -mut_file = os.path.join('data', 'pancan_mutation_freeze.tsv') -sample_freeze_file = os.path.join('data', 'sample_freeze.tsv') -cancer_gene_file = os.path.join('data', 'vogelstein_cancergenes.tsv') -copy_loss_file = os.path.join('data', 'copy_number_loss_status.tsv') -copy_gain_file = os.path.join('data', 'copy_number_gain_status.tsv') -mutation_burden_file = os.path.join('data', 'mutation_burden_freeze.tsv') +#rnaseq_file = args.x_matrix or os.path.join('data', 'pancan_rnaseq_freeze.tsv') +#mut_file = args.filename_mut or os.path.join('data', 'pancan_mutation_freeze.tsv') +#sample_freeze_file = args.filename_sample or os.path.join('data', 'sample_freeze.tsv') +#cancer_gene_file = args.filename_cancer_gene_classification or os.path.join('data', 'vogelstein_cancergenes.tsv') +#copy_loss_file = args.filename_copy_loss or os.path.join('data', 'copy_number_loss_status.tsv') +#copy_gain_file = args.filename_copy_gain or os.path.join('data', 'copy_number_gain_status.tsv') +#mutation_burden_file = args.filename_mut_burden or os.path.join('data', 'mutation_burden_freeze.tsv') + +rnaseq_file = args.x_matrix +mut_file = args.filename_mut +sample_freeze_file = args.filename_sample +cancer_gene_file = args.filename_cancer_gene_classification +copy_loss_file = args.filename_copy_loss +copy_gain_file = args.filename_copy_gain +mutation_burden_file = args.filename_mut_burden # Generate filenames to save output plots output_base_file = os.path.dirname(classifier_file) @@ -65,7 +89,11 @@ if line[0] == 'Diseases:': diseases = line[1:] if line[0] == 'Coefficients:': - coef_df = pd.read_table(line[1]) + coef_df = line[1] + # If file DNE (e.g. jobs executed with file staging), fallback to local copy + if not os.path.exists(coef_df): + coef_df = os.path.join(args.classifier, 'classifier_coefficients.tsv') + coef_df = pd.read_table(coef_df) # Subset matrix that indicates mutation status (y) common_genes = set(mutation_df.columns).intersection(genes) @@ -141,3 +169,4 @@ final_pred.loc[(final_pred.DISEASE.isin(diseases)) & (final_pred.hypermutated == 0), 'include'] = 1 final_pred.to_csv(output_dec_file, sep='\t') +print("applying weights sucessfully") \ No newline at end of file diff --git a/scripts/copy_burden_figures.R b/scripts/copy_burden_figures.R index 7a17fba..0fe435a 100644 --- a/scripts/copy_burden_figures.R +++ b/scripts/copy_burden_figures.R @@ -12,13 +12,57 @@ # Output: # Two figures summarizing copy burden across TCGA Pan Can samples +library(optparse) library(ggplot2) +# parse options +option_list = list( + make_option( + c("--version"), + action = "store_true", + default = FALSE, + help = "Print version and exit" + ), + make_option( + c("--alt_folder"), + action = "store", + default = NA, + type = 'character', + help = "Classifier base folder" + ), + make_option( + c("--junctions_with_mutations"), + action = "store", + default = NA, + type = 'character', + help = "junctions_with_mutations.csv.gz" + ) +) + +opt <-parse_args(OptionParser(option_list = option_list)) + +if (opt$version){ + # print version and exit + cat(paste("PanCancer version", toString(packageVersion("pancancer"))), "\n") + quit() +} + + # Set File Names -base_file <- file.path("classifiers", "TP53") +if ( !is.na(opt$alt_folder) ){ + base_file <- opt$alt_folder +} else { + base_file <- file.path("classifiers", "TP53") +} burden_file <- file.path(base_file, "tables", "copy_burden_predictions.tsv") -snaptron_file <- file.path("scripts", "snaptron", - "junctions_with_mutations.csv.gz") +if ( !is.na(opt$alt_folder) ){ + snaptron_file <- opt$junctions_with_mutations +} else { + snaptron_file <- file.path("scripts", "snaptron", + "junctions_with_mutations.csv.gz") +} + + frac_alt_plot <- file.path(base_file, "figures", "fraction_altered_plot.pdf") violin_plot <- file.path(base_file, "figures", "seg_altered_violin_plot.pdf") @@ -35,6 +79,9 @@ silent_junc <- silent_junc[silent_junc$snaptron_id == "13945701", ] silent_junc <- silent_junc[silent_junc$TP53 %in% 0, ] silent_junc <- silent_junc[silent_junc$include %in% 1, ] +#options for graphical changes +options(repr.plot.width=10, repr.plot.height=10, repr.plot.res = 300) + ggplot(copy_burden, aes(weight, frac_altered, color = factor(TP53))) + geom_point(alpha = 0.6, size = 0.3) + theme_bw() + xlab("TP53 Inactivation Probability") + diff --git a/scripts/copy_burden_merge.py b/scripts/copy_burden_merge.py index 27000b4..8875291 100644 --- a/scripts/copy_burden_merge.py +++ b/scripts/copy_burden_merge.py @@ -14,7 +14,7 @@ Output: .tsv file of classifier scores merged with segment based copy number scores """ - +print("copy_burden_merge process started") import os import argparse import pandas as pd @@ -22,11 +22,13 @@ parser = argparse.ArgumentParser() parser.add_argument('-c', '--classifier_folder', help='string of the location of classifier data') +parser.add_argument( '--filename_burden', default=None, + help='Filename of burden') args = parser.parse_args() # Load command arguments pred_fild = os.path.join(args.classifier_folder, 'classifier_decisions.tsv') -burden_file = os.path.join('data', 'seg_based_scores.tsv') +burden_file = args.filename_burden out_file = os.path.join(os.path.dirname(pred_fild), 'tables', 'copy_burden_predictions.tsv') @@ -38,3 +40,4 @@ right_on='Sample') combined_df.index = combined_df['Sample'] combined_df.to_csv(out_file, sep='\t') +print("copy burden merge process done") \ No newline at end of file diff --git a/scripts/map_mutation_class.py b/scripts/map_mutation_class.py index bacd37b..55a68c1 100644 --- a/scripts/map_mutation_class.py +++ b/scripts/map_mutation_class.py @@ -29,23 +29,37 @@ parser = argparse.ArgumentParser() parser.add_argument('-s', '--scores', help='string of the location of classifier scores') -parser.add_argument('-g', '--genes', - help='string of the genes to extract or genelist file') +#parser.add_argument('-g', '--genes', +# help='string of the genes to extract or genelist file') +parser.add_argument('-p', '--path_genes', + help='pathway gene list file') parser.add_argument('-c', '--copy_number', action='store_true', help='optional flag to include copy number info in map') + +parser.add_argument( '--filename_copy_loss', default=None, + help='Filename of copy number loss') +parser.add_argument( '--filename_copy_gain', default=None, + help='Filename of copy number gain') +parser.add_argument( '--filename_raw_mut', default=None, + help='Filename of raw mut MAF') + args = parser.parse_args() scores = args.scores -genes = args.genes +path_genes = args.path_genes copy_number = args.copy_number # Load command arguments prediction_file = os.path.join(scores, 'classifier_decisions.tsv') + try: - genes_df = pd.read_table(genes) - genes = genes_df['genes'].tolist() + genes_df = pd.read_csv(path_genes) + print("path_genes_file") + print(genes_df) + path_genes = genes_df['genes'].tolist() + print(path_genes) except: - genes = genes.split(',') + path_genes = path_genes.split(',') out_dir = os.path.join(os.path.dirname(prediction_file), 'tables') @@ -53,7 +67,7 @@ os.makedirs(out_dir) out_file = os.path.join(out_dir, 'mutation_classification_scores.tsv') -raw_mut_file = os.path.join('data', 'raw', 'mc3.v0.2.8.PUBLIC.maf') +raw_mut_file = args.filename_raw_mut pred_df = pd.read_table(prediction_file, index_col=0) mut_df = pd.read_table(raw_mut_file) @@ -61,7 +75,7 @@ # Process mutation file mut_df = mut_df.assign(ID=mut_df.Tumor_Sample_Barcode.str.slice(start=0, stop=15)) -sub_mut_df = mut_df[mut_df['Hugo_Symbol'].isin(genes)] +sub_mut_df = mut_df[mut_df['Hugo_Symbol'].isin(path_genes)] sub_mut_df = sub_mut_df[['ID', 'Tumor_Sample_Barcode', 'Hugo_Symbol', 'HGVSc', 'HGVSp', 'Variant_Classification']] @@ -71,8 +85,8 @@ .fillna('Wild-Type') if copy_number: # Load Copy Number info - copy_loss_file = os.path.join('data', 'copy_number_loss_status.tsv') - copy_gain_file = os.path.join('data', 'copy_number_gain_status.tsv') + copy_loss_file = args.filename_copy_loss + copy_gain_file = args.filename_copy_gain copy_loss_df = pd.read_table(copy_loss_file, index_col=0) copy_gain_df = pd.read_table(copy_gain_file, index_col=0) @@ -101,3 +115,4 @@ map_df = map_df.merge(copy_df, left_on='ID', right_on='index', how='outer') map_df.to_csv(out_file, sep='\t') +print("map_mutation class done") \ No newline at end of file diff --git a/scripts/pancancer_classifier.py b/scripts/pancancer_classifier.py index c00fa02..b04e716 100644 --- a/scripts/pancancer_classifier.py +++ b/scripts/pancancer_classifier.py @@ -55,6 +55,8 @@ import pandas as pd import csv import argparse +import matplotlib +matplotlib.use('agg') import matplotlib.pyplot as plt import seaborn as sns @@ -69,11 +71,56 @@ from tcga_util import get_args, get_threshold_metrics, integrate_copy_number from tcga_util import shuffle_columns +RASOPATHY_GENES = set(['BRAF', 'CBL', 'HRAS', 'KRAS', 'MAP2K1', 'MAP2K2', 'NF1', + 'NRAS', 'PTPN11', 'RAF1', 'SHOC2', 'SOS1', 'SPRED1', 'RIT1']) + + # Load command arguments args = get_args() -genes = args.genes.split(',') -diseases = args.diseases.split(',') +#genes = args.genes.split(',') + +# if list of the genes provided by file or comma seperated values: +try: + genes = args.genes + genes_df = pd.read_table(genes) + genes = genes_df['genes'].tolist() +except: + genes = args.genes.split(',') +print(genes) +# if list of the alt_genes provided by file or comma seperated values: +try: + alt_genes = args.alt_genes + if alt_genes is not None: + alt_genes_df = pd.read_table(alt_genes) + alt_genes = alt_genes_df['alt_genes'].tolist() +except: + alt_genes = args.alt_genes.split(',') +print(alt_genes) +# if list of the diseases provided by file or comma seperated values: +try: + diseases = args.diseases + diseases_df = pd.read_table(diseases) + diseases = diseases_df['diseases'].tolist() +except: + diseases = args.diseases.split(',') +print(diseases) +# if list of the alt_diseases provided by file or comma seperated values: +try: + alt_diseases = args.alt_diseases + if alt_diseases is not None: + alt_diseases_df = pd.read_table(alt_diseases) + alt_diseases = alt_diseases_df['alt_diseases'].tolist() +except: + alt_diseases = args.alt_diseases.split(',') +print(alt_diseases) +# if list of the genes provided by file or comma seperated values: +drop_x_genes = args.drop_x_genes +if drop_x_genes is not None: + drop_x_genes_df = pd.read_table(drop_x_genes) + drop_x_genes = patho_genes_df['drop_x_genes'].tolist() + +#diseases = args.diseases.split(',') folds = int(args.folds) drop = args.drop drop_rasopathy = args.drop_rasopathy @@ -83,10 +130,10 @@ num_features_kept = args.num_features alphas = [float(x) for x in args.alphas.split(',')] l1_ratios = [float(x) for x in args.l1_ratios.split(',')] -alt_genes = args.alt_genes.split(',') +#alt_genes = args.alt_genes.split(',') alt_filter_count = int(args.alt_filter_count) alt_filter_prop = float(args.alt_filter_prop) -alt_diseases = args.alt_diseases.split(',') +#alt_diseases = args.alt_diseases.split(',') alt_folder = args.alt_folder remove_hyper = args.remove_hyper keep_inter = args.keep_intermediate @@ -101,7 +148,8 @@ message='Changing the shape of non-C contiguous array') # Generate file names for output -genes_folder = args.genes.replace(',', '_') +#genes_folder = args.genes.replace(',', '_') +genes_folder = genes[0]+"_others" base_folder = os.path.join('classifiers', genes_folder) if alt_folder != 'Auto': @@ -128,9 +176,11 @@ classifier_file = os.path.join(base_folder, 'classifier_coefficients.tsv') roc_results_file = os.path.join(base_folder, 'pancan_roc_results.tsv') -alt_gene_base = 'alt_gene_{}_alt_disease_{}'.format( - args.alt_genes.replace(',', '_'), - args.alt_diseases.replace(',', '_')) +#alt_gene_base = 'alt_gene_{}_alt_disease_{}'.format( +# args.alt_genes.replace(',', '_'), +# args.alt_diseases.replace(',', '_')) + +alt_gene_base = 'alt_gene_alt_disease' alt_count_table_file = os.path.join(base_folder, 'alt_summary_counts.csv') alt_gene_auroc_file = os.path.join(base_folder, '{}_auroc_bar.pdf'.format(alt_gene_base)) @@ -138,17 +188,17 @@ '{}_aupr_bar.pdf'.format(alt_gene_base)) alt_gene_summary_file = os.path.join(base_folder, '{}_summary.tsv'.format(alt_gene_base)) - # Load Datasets +x_as_raw = args.x_as_raw if x_matrix == 'raw': - expr_file = os.path.join('data', 'pancan_rnaseq_freeze.tsv.gz') + expr_file = os.path.join('data', 'pancan_rnaseq_freeze.tsv') + x_as_raw = True else: expr_file = x_matrix -mut_file = os.path.join('data', 'pancan_mutation_freeze.tsv.gz') -mut_burden_file = os.path.join('data', 'mutation_burden_freeze.tsv') -sample_freeze_file = os.path.join('data', 'sample_freeze.tsv') - +mut_file = args.filename_mut or os.path.join('data', 'pancan_mutation_freeze.tsv') +mut_burden_file = args.filename_mut_burden or os.path.join('data', 'mutation_burden_freeze.tsv') +sample_freeze_file = args.filename_sample or os.path.join('data', 'sample_freeze.tsv') rnaseq_full_df = pd.read_table(expr_file, index_col=0) mutation_df = pd.read_table(mut_file, index_col=0) sample_freeze = pd.read_table(sample_freeze_file, index_col=0) @@ -156,7 +206,7 @@ # Construct data for classifier common_genes = set(mutation_df.columns).intersection(genes) -if x_matrix == 'raw': +if x_as_raw: common_genes = list(common_genes.intersection(rnaseq_full_df.columns)) else: common_genes = list(common_genes) @@ -169,27 +219,34 @@ 'are {}'.format(missing_genes), category=Warning) if drop: - if x_matrix == 'raw': + if x_as_raw: rnaseq_full_df.drop(common_genes, axis=1, inplace=True) if drop_rasopathy: - rasopathy_genes = set(['BRAF', 'CBL', 'HRAS', 'KRAS', 'MAP2K1', 'MAP2K2', - 'NF1', 'NRAS', 'PTPN11', 'RAF1', 'SHOC2', 'SOS1', - 'SPRED1', 'RIT1']) - rasopathy_drop = list(rasopathy_genes.intersection(rnaseq_full_df.columns)) - rnaseq_full_df.drop(rasopathy_drop, axis=1, inplace=True) + # Drop rasopathy genes as defined by default + drop_x_genes = RASOPATHY_GENES +else: + drop_x_genes = set() +if args.drop_x_genes: + drop_x_genes = drop_x_genes.union( set( map( lambda x: x.strip(), args.drop_x_genes.split(',') ) ) ) +if drop_x_genes: + x_drop = list(drop_x_genes.intersection(rnaseq_full_df.columns)) + rnaseq_full_df.drop(x_drop, axis=1, inplace=True) # Incorporate copy number for gene activation/inactivation if copy_number: # Load copy number matrices - copy_loss_file = os.path.join('data', 'copy_number_loss_status.tsv.gz') + #copy_loss_file = args.filename_copy_loss or os.path.join('data', 'copy_number_loss_status.tsv.gz') + copy_loss_file = args.filename_copy_loss copy_loss_df = pd.read_table(copy_loss_file, index_col=0) - copy_gain_file = os.path.join('data', 'copy_number_gain_status.tsv.gz') + #copy_gain_file = args.filename_copy_gain or os.path.join('data', 'copy_number_gain_status.tsv.gz') + copy_gain_file = args.filename_copy_gain copy_gain_df = pd.read_table(copy_gain_file, index_col=0) # Load cancer gene classification table - vogel_file = os.path.join('data', 'vogelstein_cancergenes.tsv') + #vogel_file = args.filename_cancer_gene_classification or os.path.join('data', 'vogelstein_cancergenes.tsv') + vogel_file = args.filename_cancer_gene_classification cancer_genes = pd.read_table(vogel_file) y = integrate_copy_number(y=y, cancer_genes_df=cancer_genes, @@ -241,9 +298,10 @@ y_df = y_df.loc[y_sub.index] strat = y_sub.str.cat(y_df.astype(str)) x_df = rnaseq_df.loc[y_df.index, :] - +print("strat") +print(strat) # Subset x matrix to MAD genes and scale -if x_matrix == 'raw': +if x_as_raw: med_dev = pd.DataFrame(mad(x_df), index=x_df.columns) mad_genes = med_dev.sort_values(by=0, ascending=False)\ .iloc[0:num_features_kept].index.tolist() @@ -302,6 +360,7 @@ cv_score_mat = pd.pivot_table(cv_results, values='mean_test_score', index='classify__l1_ratio', columns='classify__alpha') +plt.figure(figsize = (10,10),dpi= 300) ax = sns.heatmap(cv_score_mat, annot=True, fmt='.1%') ax.set_xlabel('Regularization strength multiplier (alpha)') ax.set_ylabel('Elastic net mixing parameter (l1_ratio)') @@ -358,6 +417,7 @@ # Plot ROC sns.set_style("whitegrid") +plt.figure(figsize = (10,10),dpi= 300) plt.figure(figsize=(3, 3)) total_auroc = {} colors = ['blue', 'green', 'orange', 'grey'] @@ -396,6 +456,7 @@ # Plot PR sns.set_style("whitegrid") +plt.figure(figsize = (10,10),dpi= 300) plt.figure(figsize=(3, 3)) total_aupr = {} colors = ['blue', 'green', 'orange', 'grey'] @@ -513,6 +574,7 @@ disease_roc_sub_file = '{}_pred_{}.pdf'.format(disease_roc_file, disease) # Plot disease specific PR + plt.figure(figsize=(3, 3)) aupr = [] idx = 0 @@ -610,7 +672,7 @@ full_roc_df.to_csv(roc_results_file, sep='\t') # Apply the same classifier previously built to predict alternative genes -if alt_genes[0] is not 'None': +if alt_genes is not None: # Classifying alternative mutations y_alt = mutation_df[alt_genes] @@ -664,7 +726,7 @@ # Process alternative x matrix x_alt_df = rnaseq_alt_df.loc[y_alt_df.index, :] - if x_matrix == 'raw': + if x_as_raw: x_alt_df = x_alt_df.loc[:, mad_genes] x_alt_df_update = pd.DataFrame(fitted_scaler.transform(x_alt_df), @@ -756,8 +818,11 @@ summarywriter.writerow(['Parameters:']) summarywriter.writerow(['Genes:'] + genes) summarywriter.writerow(['Diseases:'] + diseases) - summarywriter.writerow(['Alternative Genes:'] + alt_genes) - summarywriter.writerow(['Alternative Diseases:'] + alt_diseases) + if alt_genes is not None: + summarywriter.writerow(['Alternative Genes:'] + alt_genes) + if alt_diseases is not None: + summarywriter.writerow(['Alternative Diseases:'] + alt_diseases) + summarywriter.writerow(['Number of Features:', str(x_df.shape[1])]) summarywriter.writerow(['Drop Gene:', drop]) summarywriter.writerow(['Copy Number:', copy_number]) @@ -788,7 +853,7 @@ summarywriter.writerow(['', disease, 'Training AUPR:', aupr[0], 'Testing AUPR:', aupr[1], 'Cross Validation AUPR:', aupr[2]]) - if alt_genes[0] is not 'None': + if alt_genes is not None: summarywriter.writerow(['Alternate gene performance:'] + alt_genes) summarywriter.writerow(['Alternative gene AUROC:', str(alt_metrics_cv['auroc'])]) diff --git a/scripts/pancancer_classifier_pi3k.py b/scripts/pancancer_classifier_pi3k.py new file mode 100644 index 0000000..7e7f3d4 --- /dev/null +++ b/scripts/pancancer_classifier_pi3k.py @@ -0,0 +1,1007 @@ +""" +Gregory Way 2017 +PanCancer Classifier +scripts/pancancer_classifier.py + +Usage: Run in command line with required command argument: + + python pancancer_classifier.py --genes $GENES + +Where GENES is a comma separated string. There are also optional arguments: + + --diseases comma separated string of disease types for classifier + default: Auto (will pick diseases from filter args) + --folds number of cross validation folds + default: 5 + --drop drop the input genes from the X matrix + default: False if flag omitted + --copy_number optional flag to supplement copy number to define Y + default: False if flag omitted + --filter_count int of low count of mutation to include disease + default: 15 + --filter_prop float of low proportion of mutated samples per disease + default: 0.05 + --num_features int of number of genes to include in classifier + default: 8000 + --alphas comma separated string of alphas to test in pipeline + default: '0.1,0.15,0.2,0.5,0.8,1' + --l1_ratios comma separated string of l1 parameters to test + default: '0,0.1,0.15,0.18,0.2,0.3' + --alt_genes comma separated string of alternative genes to test + default: None + --alt_diseases comma separated string of alternative diseases to test + default: Auto + --alt_filter_count int of low count of mutations to include alt_diseases + default: 15 + --alt_filter_prop float of low proportion of mutated samples alt_disease + default: 0.05 + --alt_folder string of where to save the classifier figures + default: Auto + --remove_hyper store_true: remove hypermutated samples + default: False if flag omitted + --keep_intermediate store_true: keep intermediate roc curve items + default: False if flag omitted + --x_matrix string of which feature matrix to use + default: raw + +Output: +ROC curves, AUROC across diseases, and classifier coefficients +""" + +import os +import sys +import warnings + +import pandas as pd +import csv +import argparse +import matplotlib.pyplot as plt +import seaborn as sns + +from sklearn.linear_model import SGDClassifier +from sklearn.model_selection import train_test_split, cross_val_predict +from dask_searchcv import GridSearchCV +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler +from statsmodels.robust.scale import mad + +sys.path.insert(0, os.path.join('scripts', 'util')) +from tcga_util import get_args, get_threshold_metrics, integrate_copy_number +from tcga_util import shuffle_columns + +# Load command arguments +args = get_args() +genes = args.genes.split(',') +diseases = args.diseases.split(',') +folds = int(args.folds) +drop = args.drop +#drop_rasopathy = args.drop_rasopathy +copy_number = args.copy_number +filter_count = int(args.filter_count) +filter_prop = float(args.filter_prop) +num_features_kept = args.num_features +alphas = [float(x) for x in args.alphas.split(',')] +l1_ratios = [float(x) for x in args.l1_ratios.split(',')] +alt_genes = args.alt_genes.split(',') +alt_filter_count = int(args.alt_filter_count) +alt_filter_prop = float(args.alt_filter_prop) +alt_diseases = args.alt_diseases.split(',') +alt_folder = args.alt_folder +remove_hyper = args.remove_hyper +keep_inter = args.keep_intermediate +x_matrix = args.x_matrix +shuffled = args.shuffled +shuffled_before_training = args.shuffled_before_training +no_mutation = args.no_mutation +drop_expression = args.drop_expression +drop_covariates = args.drop_covariates + +warnings.filterwarnings('ignore', + message='Changing the shape of non-C contiguous array') + +# Generate file names for output +genes_folder = args.genes.replace(',', '_') +base_folder = os.path.join('classifiers', genes_folder) + +if alt_folder != 'Auto': + base_folder = alt_folder + +if not os.path.exists(base_folder): + os.makedirs(base_folder) +else: + warnings.warn('Classifier may have already been built! Classifier results' + ' will be overwritten!', category=Warning) + +disease_folder = os.path.join(base_folder, 'disease') +if not os.path.exists(disease_folder): + os.makedirs(disease_folder) + +count_table_file = os.path.join(base_folder, 'summary_counts.csv') +cv_heatmap_file = os.path.join(base_folder, 'cv_heatmap.pdf') +full_roc_file = os.path.join(base_folder, 'all_disease_roc.pdf') +full_pr_file = os.path.join(base_folder, 'all_disease_pr.pdf') +disease_roc_file = os.path.join(base_folder, 'disease', 'classifier_roc_') +disease_pr_file = os.path.join(base_folder, 'disease', 'classifier_pr_') +dis_summary_auroc_file = os.path.join(base_folder, 'disease_auroc.pdf') +dis_summary_aupr_file = os.path.join(base_folder, 'disease_aupr.pdf') +classifier_file = os.path.join(base_folder, 'classifier_coefficients.tsv') +roc_results_file = os.path.join(base_folder, 'pancan_roc_results.tsv') +total_status_file_1 = os.path.join(base_folder, 'total_status_1.csv') +total_status_file_2 = os.path.join(base_folder, 'total_status_2.csv') +alt_gene_base = 'alt_gene_{}_alt_disease_{}'.format( + args.alt_genes.replace(',', '_'), + args.alt_diseases.replace(',', '_')) +alt_count_table_file = os.path.join(base_folder, 'alt_summary_counts.csv') +alt_gene_auroc_file = os.path.join(base_folder, + '{}_auroc_bar.pdf'.format(alt_gene_base)) +alt_gene_aupr_file = os.path.join(base_folder, + '{}_aupr_bar.pdf'.format(alt_gene_base)) +alt_gene_summary_file = os.path.join(base_folder, + '{}_summary.tsv'.format(alt_gene_base)) + +print("file names generated for outputs") + +#input("press Enter") + +# Load Datasets +if x_matrix == 'raw': + expr_file = os.path.join('data', 'pancan_rnaseq_freeze.tsv') + print("loaded pancan_rnaseq_freeze.tsv") +else: + expr_file = x_matrix + +print("x_matrix == raw") + +mut_file = os.path.join('data', 'pancan_mutation_freeze.tsv.gz') +print("loaded pancan_mutation_freeze.tsv.gz") + +mut_burden_file = os.path.join('data', 'mutation_burden_freeze.tsv') +print("loaded mutation_burden_freeze.tsv") + +sample_freeze_file = os.path.join('data', 'sample_freeze.tsv') +print("loaded sample_freeze.tsv") + +rnaseq_full_df = pd.read_table(expr_file, index_col=0) + +mutation_df = pd.read_table(mut_file, index_col=0) + +sample_freeze = pd.read_table(sample_freeze_file, index_col=0) +mut_burden = pd.read_table(mut_burden_file) +print("read all input files") + +# Construct data for classifier +common_genes = set(mutation_df.columns).intersection(genes) +if x_matrix == 'raw': + common_genes = list(common_genes.intersection(rnaseq_full_df.columns)) +else: + common_genes = list(common_genes) +print("common_genes line_178") +print(common_genes) +print("mutation_df line_180") +print(mutation_df) +#if gene has mutation, the status is 1 or without mutation 0, even multiple mutations ..the status is 1 +y = mutation_df[common_genes] +print("y,line_184") +print(y) + +missing_genes = set(genes).difference(common_genes) +print("missing_genes_line 188") +print(missing_genes) + +if len(common_genes) != len(genes): + warnings.warn('All input genes were not found in data. The missing genes ' + 'are {}'.format(missing_genes), category=Warning) +if drop: + if x_matrix == 'raw': + rnaseq_full_df.drop(common_genes, axis=1, inplace=True) +print("x_matrix constructed line_197") +print(x_matrix) + +#no diseases called as pi3kpathy like rasopathy, can delete this step? +# +#if drop_rasopathy: +# rasopathy_genes = set(['BRAF', 'CBL', 'HRAS', 'KRAS', 'MAP2K1', 'MAP2K2', +# 'NF1', 'NRAS', 'PTPN11', 'RAF1', 'SHOC2', 'SOS1', +# 'SPRED1', 'RIT1']) +# rasopathy_drop = list(rasopathy_genes.intersection(rnaseq_full_df.columns)) +# rnaseq_full_df.drop(rasopathy_drop, axis=1, inplace=True) + +# Incorporate copy number for gene activation/inactivation +if copy_number: + # Load copy number matrices + copy_loss_file = os.path.join('data', 'copy_number_loss_status.tsv.gz') + copy_loss_df = pd.read_table(copy_loss_file, index_col=0) + print("loaded copy number matrices") + copy_gain_file = os.path.join('data', 'copy_number_gain_status.tsv.gz') + copy_gain_df = pd.read_table(copy_gain_file, index_col=0) + + # Load cancer gene classification table + vogel_file = os.path.join('data', 'vogelstein_cancergenes.tsv') + cancer_genes = pd.read_table(vogel_file) + print("loaded cancer gene classification table") + y = integrate_copy_number(y=y, cancer_genes_df=cancer_genes, + genes=common_genes, loss_df=copy_loss_df, + gain_df=copy_gain_df, + include_mutation=True) + +print("copy_number, cancer_genes integrated") +print("y,line_228") +print(y) + +# Process y matrix including mutation per counts and total proportion +y = y.assign(total_status=y.max(axis=1)) +print("y line_233") +print(y) +y.to_csv(total_status_file_1) +y = y.reset_index().merge(sample_freeze, + how='left').set_index('SAMPLE_BARCODE') +print("y line_238") +print(y) +y.to_csv(total_status_file_2) +count_df = y.groupby('DISEASE').sum() +print("count_df line_242") +print(count_df) +#estimating proportion +prop_df = count_df.divide(y['DISEASE'].value_counts(sort=False).sort_index(), + axis=0) + +print(y['DISEASE'].value_counts(sort=False)) + +print("prop_df line_250") +print(prop_df) + +count_table = count_df.merge(prop_df, left_index=True, right_index=True, + suffixes=('_count', '_proportion')) +count_table.to_csv(count_table_file) +print("y_matrix constructed") + +# Filter diseases +mut_count = count_df['total_status'] +prop = prop_df['total_status'] + +if diseases[0] == 'Auto': + filter_disease = (mut_count > filter_count) & (prop > filter_prop) + diseases = filter_disease.index[filter_disease].tolist() +print("Filtered diseases") +print("mut_count line_259") +print(mut_count) +print("filter_count line_263") +print(filter_count) + +# Load mutation burden and process covariates +y_df = y[y.DISEASE.isin(diseases)].total_status +common_samples = list(set(y_df.index) & set(rnaseq_full_df.index)) +y_df = y_df.loc[common_samples] +print("y_df indexed by common_samples _line 272") +print(y_df) +print(" common_samples line 273") +print(common_samples) +rnaseq_df = rnaseq_full_df.loc[y_df.index, :] +print("rnaseq_df ref to common_samples line 275") +print(rnaseq_df) +#removing hyper mutational burden by using standard deviation +if remove_hyper: + burden_filter = mut_burden['log10_mut'] < 5 * mut_burden['log10_mut'].std() + mut_burden = mut_burden[burden_filter] + +y_matrix = mut_burden.merge(pd.DataFrame(y_df), right_index=True, + left_on='SAMPLE_BARCODE')\ + .set_index('SAMPLE_BARCODE') + +print("y_matrix merged with mutation burdenline 291") +print(y_matrix) + +# Add covariate information (cancer-type dummy variables and per sample log10 mutation count) +y_sub = y.loc[y_matrix.index]['DISEASE'] +print("y_sub indexed to disease line 295") +print(y_sub) +covar_dummy = pd.get_dummies(sample_freeze['DISEASE']).astype(int) +covar_dummy.index = sample_freeze['SAMPLE_BARCODE'] +covar = covar_dummy.merge(y_matrix, right_index=True, left_index=True) +covar = covar.drop('total_status', axis=1) +print("Added covariate information") +print("covar for disease and merged to y_matrix") +print(covar) + +# How cross validation splits will be balanced and stratified +y_df = y_df.loc[y_sub.index] +print("y_df indexed to y_sub line 308") +print(y_df) +strat = y_sub.str.cat(y_df.astype(str)) +print("strat line 311") +print(strat) +x_df = rnaseq_df.loc[y_df.index, :] +print("x_df indexed to y_df line 312") +print(x_df) + +# Subset x matrix to MAD genes and scale +if x_matrix == 'raw': + med_dev = pd.DataFrame(mad(x_df), index=x_df.columns) + mad_genes = med_dev.sort_values(by=0, ascending=False)\ + .iloc[0:num_features_kept].index.tolist() + x_df = x_df.loc[:, mad_genes] +fitted_scaler = StandardScaler().fit(x_df) + +x_df_update = pd.DataFrame(fitted_scaler.transform(x_df), + columns=x_df.columns) +x_df_update.index = x_df.index +x_df = x_df_update.merge(covar, left_index=True, right_index=True) + +print("Subseted x matrix to MAD genes and scaled") +print("x_df merged with covar and mad_genes line 329") +print(x_df) + +# Remove information from the X matrix given input arguments +if drop_expression: + x_df = x_df.iloc[:, num_features_kept:] +elif drop_covariates: + x_df = x_df.iloc[:, 0:num_features_kept] + +print("removed drop_expression or drop_covariates from the X matrix by given input arguments") + +print("x_df drop expression or drop_covariates line 340") +print(x_df) + +# Shuffle expression matrix _before_ training - this can be used as NULL model +if shuffled_before_training: + # Shuffle genes + x_train_genes = x_df.iloc[:, range(num_features_kept)] + rnaseq_shuffled_df = x_train_genes.apply(shuffle_columns, axis=1, + result_type='broadcast') + + x_train_cov = x_df.iloc[:, num_features_kept:] + x_df = pd.concat([rnaseq_shuffled_df, x_train_cov], axis=1) + +print("Shuffled expression matrix _before_ training") +print("x_df shuffled before training_line 354") +print(x_df) +print("y_df, strat from line 307, line 310") +print(y_df) + +# Build classifier pipeline +print("starting to build classifier") +x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, + test_size=0.1, + random_state=0, + stratify=strat) +print("training and testing sets line 366") +print("x_train") +print(x_train) +print("x_test") +print(x_test) +print("y_train") +print(y_train) +print("y_test") +print(y_test) + +clf_parameters = {'classify__loss': ['log'], + 'classify__penalty': ['elasticnet'], + 'classify__alpha': alphas, 'classify__l1_ratio': l1_ratios} +print("clf_parameters line 376") +print(clf_parameters) + +estimator = Pipeline(steps=[('classify', SGDClassifier(random_state=0, + class_weight='balanced', + loss='log', + max_iter=5, + tol=None))]) +print("estimator line 382") +print(estimator) +print("Applied Stochastic Gradient Descent classifier") + +cv_pipeline = GridSearchCV(estimator=estimator, param_grid=clf_parameters, + n_jobs=-1, cv=folds, scoring='roc_auc', + return_train_score=True) +cv_pipeline.fit(X=x_train, y=y_train) +print("cv_pipeline line 391") +print(cv_pipeline) +cv_results = pd.concat([pd.DataFrame(cv_pipeline.cv_results_) + .drop('params', axis=1), + pd.DataFrame.from_records(cv_pipeline + .cv_results_['params'])], + axis=1) +print("cv_results line 402") +print(cv_results) +cv_results_file = os.path.join(base_folder, 'cv_results.csv') +cv_results.to_csv(cv_results_file) +print("Fitting and Tuning") + +# Cross-validated performance heatmap +cv_score_mat = pd.pivot_table(cv_results, values='mean_test_score', + index='classify__l1_ratio', + columns='classify__alpha') +ax = sns.heatmap(cv_score_mat, annot=True, fmt='.1%') +ax.set_xlabel('Regularization strength multiplier (alpha)') +ax.set_ylabel('Elastic net mixing parameter (l1_ratio)') +plt.tight_layout() +plt.savefig(cv_heatmap_file, dpi=600, bbox_inches='tight') +plt.close() + +print("Cross-validated performance heatmap generated") + +# Get predictions +y_predict_train = cv_pipeline.decision_function(x_train) +y_predict_test = cv_pipeline.decision_function(x_test) +metrics_train = get_threshold_metrics(y_train, y_predict_train, + drop_intermediate=keep_inter) +metrics_test = get_threshold_metrics(y_test, y_predict_test, + drop_intermediate=keep_inter) + +print("y_predict_train line 420-425") +print(y_predict_train) +print("y_predict_test") +print(y_predict_test) +print("metrics_train") +print(metrics_train) +print("metrics_test") +print(metrics_test) + +# Rerun "cross validation" for the best hyperparameter set to define +# cross-validation disease-specific performance. Each sample prediction is +# based on the fold that the sample was in the testing partition +y_cv = cross_val_predict(cv_pipeline.best_estimator_, X=x_train, y=y_train, + cv=folds, method='decision_function') +print("y_cv best cross-validation prediction line 434") +print(y_cv) +metrics_cv = get_threshold_metrics(y_train, y_cv, + drop_intermediate=keep_inter) +print("metrics_cv line 443") +print(metrics_cv) +print("predictions and cross validation for cross-validation disease specific performance") + +# Determine shuffled predictive ability of shuffled gene expression matrix +# representing a test of inflation of ROC metrics. Be sure to only shuffle +# gene names, retain covariate information (tissue type and log10 mutations) +if shuffled: + # Shuffle genes + x_train_genes = x_train.iloc[:, range(num_features_kept)] + rnaseq_shuffled_df = x_train_genes.apply(shuffle_columns, axis=1, + result_type='broadcast') + + x_train_cov = x_train.iloc[:, num_features_kept:] + rnaseq_shuffled_df = pd.concat([rnaseq_shuffled_df, x_train_cov], axis=1) + + y_predict_shuffled = cv_pipeline.decision_function(rnaseq_shuffled_df) + metrics_shuffled = get_threshold_metrics(y_train, y_predict_shuffled, + drop_intermediate=keep_inter) +print("predictive ability of shuffled gene expression matrix") + +# Decide to save ROC results to file +if keep_inter: + train_roc = metrics_train['roc_df'] + train_roc = train_roc.assign(train_type='train') + test_roc = metrics_test['roc_df'] + test_roc = test_roc.assign(train_type='test') + cv_roc = metrics_cv['roc_df'] + cv_roc = cv_roc.assign(train_type='cv') + full_roc_df = pd.concat([train_roc, test_roc, cv_roc]) + if shuffled: + shuffled_roc = metrics_shuffled['roc_df'] + shuffled_roc = shuffled_roc.assign(train_type='shuffled') + full_roc_df = pd.concat([full_roc_df, shuffled_roc]) + full_roc_df = full_roc_df.assign(disease='PanCan') +print("ROC results to file") + +# Plot ROC +sns.set_style("whitegrid") +plt.figure(figsize=(3, 3)) +total_auroc = {} +colors = ['blue', 'green', 'orange', 'grey'] +idx = 0 + +metrics_list = [('Training', metrics_train), ('Testing', metrics_test), + ('CV', metrics_cv)] +if shuffled: + metrics_list += [('Random', metrics_shuffled)] + +for label, metrics in metrics_list: + + roc_df = metrics['roc_df'] + plt.plot(roc_df.fpr, roc_df.tpr, + label='{} (AUROC = {:.1%})'.format(label, metrics['auroc']), + linewidth=1, c=colors[idx]) + total_auroc[label] = metrics['auroc'] + idx += 1 + +plt.axis('equal') +plt.plot([0, 1], [0, 1], color='navy', linewidth=1, linestyle='--') +plt.xlim([0.0, 1.0]) +plt.ylim([0.0, 1.05]) +plt.xlabel('False Positive Rate', fontsize=8) +plt.ylabel('True Positive Rate', fontsize=8) +plt.title('') +plt.tick_params(labelsize=8) +lgd = plt.legend(bbox_to_anchor=(1.03, 0.85), + loc=2, + borderaxespad=0., + fontsize=7.5) + +plt.savefig(full_roc_file, dpi=600, bbox_extra_artists=(lgd,), + bbox_inches='tight') +plt.close() +print("ROC plotted") + +# Plot PR +sns.set_style("whitegrid") +plt.figure(figsize=(3, 3)) +total_aupr = {} +colors = ['blue', 'green', 'orange', 'grey'] +idx = 0 + +metrics_list = [('Training', metrics_train), ('Testing', metrics_test), + ('CV', metrics_cv)] +if shuffled: + metrics_list += [('Random', metrics_shuffled)] + +for label, metrics in metrics_list: + pr_df = metrics['pr_df'] + plt.plot(pr_df.recall, pr_df.precision, + label='{} (AUPR = {:.1%})'.format(label, metrics['aupr']), + linewidth=1, c=colors[idx]) + total_aupr[label] = metrics['aupr'] + idx += 1 + +plt.axis('equal') +plt.xlim([0.0, 1.0]) +plt.ylim([0.0, 1.05]) +plt.xlabel('Recall', fontsize=8) +plt.ylabel('Precision', fontsize=8) +plt.title('') +plt.tick_params(labelsize=8) +lgd = plt.legend(bbox_to_anchor=(1.03, 0.85), + loc=2, + borderaxespad=0., + fontsize=7.5) + +plt.savefig(full_pr_file, dpi=600, bbox_extra_artists=(lgd,), + bbox_inches='tight') +plt.close() +print("PR plotted") + +# disease specific performance +print("starting disease specific performance") +disease_metrics = {} +for disease in diseases: + # Get all samples in current disease + print("Get all samples in current disease") + sample_sub = y_sub[y_sub == disease].index + + # Get true and predicted training labels + print("Get true and predicted training labels") + y_disease_train = y_train[y_train.index.isin(sample_sub)] + if y_disease_train.sum() < 1: + continue + y_disease_predict_train = y_predict_train[y_train.index.isin(sample_sub)] + + # Get true and predicted testing labels + print("Get true and predicted testing labels") + y_disease_test = y_test[y_test.index.isin(sample_sub)] + if y_disease_test.sum() < 1: + continue + y_disease_predict_test = y_predict_test[y_test.index.isin(sample_sub)] + + # Get predicted labels for samples when they were in cross validation set + # The true labels are y_pred_train + print("Get predicted labels for samples when they were in cross validation set") + print("The true labels are y_pred_train") + y_disease_predict_cv = y_cv[y_train.index.isin(sample_sub)] + + # Get classifier performance metrics for three scenarios for each disease + print("Get classifier performance metrics for three scenarios for each disease") + met_train_dis = get_threshold_metrics(y_disease_train, + y_disease_predict_train, + disease=disease, + drop_intermediate=keep_inter) + met_test_dis = get_threshold_metrics(y_disease_test, + y_disease_predict_test, + disease=disease, + drop_intermediate=keep_inter) + met_cv_dis = get_threshold_metrics(y_disease_train, + y_disease_predict_cv, + disease=disease, + drop_intermediate=keep_inter) + + # Get predictions and metrics with shuffled gene expression + print("Get predictions and metrics with shuffled gene expression") + if shuffled: + y_dis_predict_shuf = y_predict_shuffled[y_train.index.isin(sample_sub)] + met_shuff_dis = get_threshold_metrics(y_disease_train, + y_dis_predict_shuf, + disease=disease, + drop_intermediate=keep_inter) + + if keep_inter: + train_roc = met_train_dis['roc_df'] + train_roc = train_roc.assign(train_type='train') + test_roc = met_test_dis['roc_df'] + test_roc = test_roc.assign(train_type='test') + cv_roc = met_cv_dis['roc_df'] + cv_roc = cv_roc.assign(train_type='cv') + full_dis_roc_df = train_roc.append(test_roc).append(cv_roc) + + if shuffled: + shuffled_roc = met_shuff_dis['roc_df'] + shuffled_roc = shuffled_roc.assign(train_type='shuffled') + full_dis_roc_df = full_dis_roc_df.append(shuffled_roc) + + full_dis_roc_df = full_dis_roc_df.assign(disease=disease) + full_roc_df = full_roc_df.append(full_dis_roc_df) + + # Store results in disease indexed dictionary + print("Store results in disease indexed dictionary") + disease_metrics[disease] = [met_train_dis, met_test_dis, met_cv_dis] + + if shuffled: + disease_metrics[disease] += [met_shuff_dis] + +disease_auroc = {} +disease_aupr = {} +for disease, metrics_val in disease_metrics.items(): + + labels = ['Training', 'Testing', 'CV', 'Random'] + met_list = [] + idx = 0 + for met in metrics_val: + lab = labels[idx] + met_list.append((lab, met)) + idx += 1 + + disease_pr_sub_file = '{}_pred_{}.pdf'.format(disease_pr_file, disease) + disease_roc_sub_file = '{}_pred_{}.pdf'.format(disease_roc_file, disease) + + # Plot disease specific PR + print("Plotting disease specific PR") + plt.figure(figsize=(3, 3)) + aupr = [] + idx = 0 + for label, metrics in met_list: + pr_df = metrics['pr_df'] + plt.plot(pr_df.recall, pr_df.precision, + label='{} (AUPR = {:.1%})'.format(label, metrics['aupr']), + linewidth=1, c=colors[idx]) + aupr.append(metrics['aupr']) + idx += 1 + disease_aupr[disease] = aupr + + plt.axis('equal') + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('Recall', fontsize=8) + plt.ylabel('Precision', fontsize=8) + plt.title('') + plt.tick_params(labelsize=8) + lgd = plt.legend(bbox_to_anchor=(1.03, 0.85), + loc=2, + borderaxespad=0., + fontsize=7.5) + + plt.savefig(disease_pr_sub_file, dpi=600, bbox_extra_artists=(lgd,), + bbox_inches='tight') + plt.close() + + # Plot disease specific ROC + print("Plotting disease specific ROC") + plt.figure(figsize=(3, 3)) + auroc = [] + idx = 0 + for label, metrics in met_list: + roc_df = metrics['roc_df'] + plt.plot(roc_df.fpr, roc_df.tpr, + label='{} (AUROC = {:.1%})'.format(label, metrics['auroc']), + linewidth=1, c=colors[idx]) + auroc.append(metrics['auroc']) + idx += 1 + disease_auroc[disease] = auroc + + plt.axis('equal') + plt.plot([0, 1], [0, 1], color='navy', linewidth=1, linestyle='--') + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('False Positive Rate', fontsize=8) + plt.ylabel('True Positive Rate', fontsize=8) + plt.title('') + plt.tick_params(labelsize=8) + lgd = plt.legend(bbox_to_anchor=(1.03, 0.85), + loc=2, + borderaxespad=0., + fontsize=7.5) + + plt.savefig(disease_roc_sub_file, dpi=600, bbox_extra_artists=(lgd,), + bbox_inches='tight') + plt.close() + +index_lab = ['Train', 'Test', 'Cross Validation'] + +if shuffled: + index_lab += ['Random'] + +disease_auroc_df = pd.DataFrame(disease_auroc, index=index_lab).T +disease_auroc_df = disease_auroc_df.sort_values('Cross Validation', + ascending=False) +ax = disease_auroc_df.plot(kind='bar', title='Disease Specific Performance') +ax.set_ylabel('AUROC') +plt.tight_layout() +plt.savefig(dis_summary_auroc_file, dpi=600, bbox_inches='tight') +plt.close() +print("disease_auroc_df line_715") +print(disease_auroc_df) + +disease_aupr_df = pd.DataFrame(disease_aupr, index=index_lab).T +disease_aupr_df = disease_aupr_df.sort_values('Cross Validation', + ascending=False) +ax = disease_aupr_df.plot(kind='bar', title='Disease Specific Performance') +ax.set_ylabel('AUPR') +plt.tight_layout() +plt.savefig(dis_summary_aupr_file, dpi=600, bbox_inches='tight') +plt.close() + +print("disease_aupr_df line_726") +print(disease_aupr_df) +# Save classifier coefficients +print("Saving classifier coefficients") +final_pipeline = cv_pipeline.best_estimator_ +final_classifier = final_pipeline.named_steps['classify'] + +coef_df = pd.DataFrame.from_dict( + {'feature': x_df.columns, + 'weight': final_classifier.coef_[0]}) + +coef_df['abs'] = coef_df['weight'].abs() +coef_df = coef_df.sort_values('abs', ascending=False) +coef_df.to_csv(classifier_file, sep='\t') + +if keep_inter: + full_roc_df.to_csv(roc_results_file, sep='\t') + +# Apply the same classifier previously built to predict alternative genes +print("Applying the same classifier previously built to predict alternative genes") +if alt_genes[0] is not 'None': + # Classifying alternative mutations + print("Classifying alternative mutations") + y_alt = mutation_df[alt_genes] + print("y_alt line_759") + print(y_alt) + # Add copy number info if applicable + print("Add copy number info if applicable for alternative genes") + if copy_number: + y_alt = integrate_copy_number(y=y_alt, cancer_genes_df=cancer_genes, + genes=alt_genes, loss_df=copy_loss_df, + gain_df=copy_gain_df) + print("y_alt line_764") + print(y_alt) + + # Append disease id + print("Appending disease id for alternative genes") + y_alt = y_alt.assign(total_status=y_alt.max(axis=1)) + y_alt = y_alt.reset_index().merge(sample_freeze, + how='left').set_index('SAMPLE_BARCODE') + + # Filter data + print("Filter data by disease for alternative genes") + alt_count_df = y_alt.groupby('DISEASE').sum() + alt_prop_df = alt_count_df.divide(y_alt['DISEASE'].value_counts(sort=False) + .sort_index(), axis=0) + + alt_count_table = alt_count_df.merge(alt_prop_df, + left_index=True, + right_index=True, + suffixes=('_count', '_proportion')) + alt_count_table.to_csv(alt_count_table_file) + + mut_co = alt_count_df['total_status'] + prop = alt_prop_df['total_status'] + + if alt_diseases[0] == 'Auto': + alt_filter_dis = (mut_co > alt_filter_count) & (prop > alt_filter_prop) + alt_diseases = alt_filter_dis.index[alt_filter_dis].tolist() + + # Subset data + print("Subsetting data") + y_alt_df = y_alt[y_alt.DISEASE.isin(alt_diseases)].total_status + common_alt_samples = list(set(y_alt_df.index) & set(rnaseq_full_df.index)) + + y_alt_df = y_alt_df.loc[common_alt_samples] + rnaseq_alt_df = rnaseq_full_df.loc[y_alt_df.index, :] + + y_alt_matrix = mut_burden.merge(pd.DataFrame(y_alt_df), right_index=True, + left_on='SAMPLE_BARCODE')\ + .set_index('SAMPLE_BARCODE') + print("y_alt_df_line 802") + print(y_alt_df) + + # Add Covariate Info to alternative y matrix + print("Adding Covariate Info to alternative y matrix") + y_alt_sub = y_alt.loc[y_alt_matrix.index]['DISEASE'] + covar_dummy_alt = pd.get_dummies(sample_freeze['DISEASE']).astype(int) + covar_dummy_alt.index = sample_freeze['SAMPLE_BARCODE'] + covar_alt = covar_dummy_alt.merge(y_alt_matrix, right_index=True, + left_index=True) + covar_alt = covar_alt.drop('total_status', axis=1) + y_alt_df = y_alt_df.loc[y_alt_sub.index] + + # Process alternative x matrix + print("Processing alternative x matrix line_819") + x_alt_df = rnaseq_alt_df.loc[y_alt_df.index, :] + if x_matrix == 'raw': + x_alt_df = x_alt_df.loc[:, mad_genes] + + x_alt_df_update = pd.DataFrame(fitted_scaler.transform(x_alt_df), + columns=x_alt_df.columns) + x_alt_df_update.index = x_alt_df.index + x_alt_df = x_alt_df_update.merge(covar_alt, left_index=True, + right_index=True) + print("x_alt_df alternative x matrix line_829") + print(x_alt_df) + + # Apply the previously fit model to predict the alternate Y matrix + print("Applying the previously fit model to predict the alternate Y matrix") + y_alt_cv = cv_pipeline.decision_function(X=x_alt_df) + alt_metrics_cv = get_threshold_metrics(y_alt_df, y_alt_cv, + drop_intermediate=keep_inter) + + validation_metrics = {} + val_x_type = {} + + for disease in alt_diseases: + sample_dis = y_alt_sub[y_alt_sub == disease].index + print("sample_dis line_845") + print(sample_dis) + + # Subset full data if it has not been trained on + + if disease not in diseases: + print(disease) + x_sub = x_alt_df.loc[sample_dis] + y_sub = y_alt_df[sample_dis] + category = 'Full' + print("x_sub Full line_849") + print(x_sub) + print("y_sub Full line_850") + print(y_sub) + # Only subset to the holdout set if data was trained on + else: + print(disease) + x_sub = x_test.loc[x_test.index.isin(sample_dis)] + y_sub = y_test[y_test.index.isin(sample_dis)] + category = 'Holdout' + print("x_sub Holdout line_858") + print(x_sub) + print("y_sub Holdout line_859") + print(y_sub) + print("If there are not enough classes do not proceed to plot") + # If there are not enough classes do not proceed to plot + if y_sub.sum() < 1: + print("y_sub sum less than 1 line_873") + print(y_sub) + continue + + neg, pos = y_sub.value_counts() + val_x_type[disease] = [category, neg, pos] + print("val_x_type line_878") + print(val_x_type) + y_pred_alt = cv_pipeline.decision_function(x_sub) + y_pred_alt_cv = y_alt_cv[y_alt_df.index.isin(y_sub.index)] + + alt_metrics_dis = get_threshold_metrics(y_sub, y_pred_alt, + disease=disease, + drop_intermediate=keep_inter) + alt_metrics_di_cv = get_threshold_metrics(y_sub, y_pred_alt_cv, + disease=disease, + drop_intermediate=keep_inter) + validation_metrics[disease] = [alt_metrics_dis, alt_metrics_di_cv] + + print("Compiling a alternative summary dataframe") + + # Compile a summary dataframe + val_x_type = pd.DataFrame.from_dict(val_x_type) + val_x_type.index = ['class', 'negatives', 'positives'] + val_x_type.to_csv(alt_gene_summary_file, sep='\t') + + alt_disease_auroc = {} + alt_disease_aupr = {} + for disease, metrics_val in validation_metrics.items(): + met_test, met_cv = metrics_val + alt_disease_auroc[disease] = [met_test['auroc'], met_cv['auroc']] + alt_disease_aupr[disease] = [met_test['aupr'], met_cv['aupr']] + + print("Plotting alternative gene cancer-type specific AUROC plots") + + # Plot alternative gene cancer-type specific AUROC plots + alt_disease_auroc_df = pd.DataFrame(alt_disease_auroc, + index=['Hold Out', 'Full Data']).T + alt_disease_auroc_df = alt_disease_auroc_df.sort_values('Full Data', + ascending=False) + ax = alt_disease_auroc_df.plot(kind='bar', title='Alt Gene Performance') + ax.set_ylim([0, 1]) + ax.set_ylabel('AUROC') + plt.tight_layout() + plt.savefig(alt_gene_auroc_file, dpi=600, bbox_inches='tight') + plt.close() + + print("Plot alternative gene cancer-type specific AUPR plots") + + # Plot alternative gene cancer-type specific AUPR plots + + alt_disease_aupr_df = pd.DataFrame(alt_disease_aupr, + index=['Hold Out', 'Full Data']).T + alt_disease_aupr_df = alt_disease_aupr_df.sort_values('Full Data', + ascending=False) + ax = alt_disease_aupr_df.plot(kind='bar', title='Alt Gene Performance') + ax.set_ylim([0, 1]) + ax.set_ylabel('AUPR') + plt.tight_layout() + plt.savefig(alt_gene_aupr_file, dpi=600, bbox_inches='tight') + plt.close() + + +# Write a summary for the inputs and outputs of the classifier +print("Write a summary for the inputs and outputs of the classifier") +with open(os.path.join(base_folder, 'classifier_summary.txt'), 'w') as sum_fh: + summarywriter = csv.writer(sum_fh, delimiter='\t') + + print("Summarizing parameters and classifier_summary.txt file") + + # Summarize parameters + summarywriter.writerow(['Parameters:']) + summarywriter.writerow(['Genes:'] + genes) + summarywriter.writerow(['Diseases:'] + diseases) + summarywriter.writerow(['Alternative Genes:'] + alt_genes) + summarywriter.writerow(['Alternative Diseases:'] + alt_diseases) + summarywriter.writerow(['Number of Features:', str(x_df.shape[1])]) + summarywriter.writerow(['Drop Gene:', drop]) + summarywriter.writerow(['Copy Number:', copy_number]) + summarywriter.writerow(['Alphas:'] + alphas) + summarywriter.writerow(['L1_ratios:'] + l1_ratios) + summarywriter.writerow(['Hypermutated Removed:', str(remove_hyper)]) + summarywriter.writerow([]) + + print("Summaryizing results") + + # Summaryize results + summarywriter.writerow(['Results:']) + summarywriter.writerow(['Optimal Alpha:', + str(cv_pipeline.best_params_['classify__alpha'])]) + summarywriter.writerow(['Optimal L1:', str(cv_pipeline.best_params_ + ['classify__l1_ratio'])]) + summarywriter.writerow(['Coefficients:', classifier_file]) + summarywriter.writerow(['Training AUROC:', metrics_train['auroc']]) + summarywriter.writerow(['Testing AUROC:', metrics_test['auroc']]) + summarywriter.writerow(['Cross Validation AUROC', metrics_cv['auroc']]) + summarywriter.writerow(['Training AUPR:', metrics_train['aupr']]) + summarywriter.writerow(['Testing AUPR:', metrics_test['aupr']]) + summarywriter.writerow(['Cross Validation AUPR:', metrics_cv['aupr']]) + summarywriter.writerow(['Disease specific performance:']) + for disease, auroc in disease_auroc.items(): + summarywriter.writerow(['', disease, 'Training AUROC:', auroc[0], + 'Testing AUROC:', auroc[1], + 'Cross Validation AUROC:', auroc[2]]) + for disease, aupr in disease_aupr.items(): + summarywriter.writerow(['', disease, 'Training AUPR:', aupr[0], + 'Testing AUPR:', aupr[1], + 'Cross Validation AUPR:', aupr[2]]) + if alt_genes[0] is not 'None': + summarywriter.writerow(['Alternate gene performance:'] + alt_genes) + summarywriter.writerow(['Alternative gene AUROC:', + str(alt_metrics_cv['auroc'])]) + summarywriter.writerow(['Alternative gene AUPR:', + str(alt_metrics_cv['aupr'])]) + for alt_dis, alt_auroc in alt_disease_auroc.items(): + summarywriter.writerow(['', alt_dis, + 'Holdout AUROC:', alt_auroc[0], + 'Full Data AUROC:', alt_auroc[1], + 'Category:', val_x_type[alt_dis]['class'], + 'num_positive:', + str(val_x_type[alt_dis]['positives']), + 'num_negatives:', + str(val_x_type[alt_dis]['negatives'])]) + for alt_dis, alt_aupr in alt_disease_aupr.items(): + summarywriter.writerow(['', alt_dis, + 'Holdout AUPR:', alt_aupr[0], + 'Full Data AUPR:', alt_aupr[1], + 'Category:', val_x_type[alt_dis]['class'], + 'num_positive:', + str(val_x_type[alt_dis]['positives']), + 'num_negatives:', + str(val_x_type[alt_dis]['negatives'])]) + +print("pancancer_classifier_pi3k_function executed sucessfully") \ No newline at end of file diff --git a/scripts/pi3k_ccle_pharmacology2.ipynb b/scripts/pi3k_ccle_pharmacology2.ipynb new file mode 100644 index 0000000..7f2f2df --- /dev/null +++ b/scripts/pi3k_ccle_pharmacology2.ipynb @@ -0,0 +1,1375 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "suppressPackageStartupMessages({\n", + " library(dplyr)\n", + " library(ggplot2)\n", + " library(ggpmisc)})" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“Missing column names filled in: 'X1' [1]”Parsed with column specification:\n", + "cols(\n", + " .default = col_character(),\n", + " `Num Data` = col_integer(),\n", + " `EC50 (uM)` = col_double(),\n", + " `IC50 (uM)` = col_double(),\n", + " Amax = col_double(),\n", + " ActArea = col_double(),\n", + " AKT1_MUT = col_integer(),\n", + " PIK3CA_MUT = col_integer(),\n", + " KRAS_MUT = col_integer(),\n", + " ERBB2_MUT = col_integer(),\n", + " BRAF_MUT = col_integer(),\n", + " pi3k_status = col_integer(),\n", + " weight = col_double()\n", + ")\n", + "See spec(...) for full column specifications.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
X1Primary Cell Line NameCompoundTargetDoses (uM)Activity Data (median)Activity SDNum DataFitTypeEC50 (uM)⋯tissueAKT1_MUTPIK3CA_MUTKRAS_MUTERBB2_MUTBRAF_MUTpi3k_statusweightsample_namepredictions
A172_CENTRAL_NERVOUS_SYSTEM A172 AEW541 IGF1R .0025,.0080,.025,.080,.25,.80,2.53,8 -.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-22 5.84,1.59,2.77,5.89,2.43,3.25,4.54,7.738 Sigmoid 8.4196215 ⋯ CENTRAL NERVOUS SYSTEM 0 0 0 0 0 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant
A172_CENTRAL_NERVOUS_SYSTEM A172 Nilotinib ABL .0025,.0080,.025,.080,.25,.80,2.53,8 -.75,3.15,9.45,7.81,1.15,4.57,-3.7,-2.16.44,3.36,2.34,1.18,1.86,3.21,4.71,6.228 Constant NA ⋯ CENTRAL NERVOUS SYSTEM 0 0 0 0 0 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant
A172_CENTRAL_NERVOUS_SYSTEM A172 17-AAG HSP90 .0025,.0080,.025,.080,.25,.80,2.53,8 -4.1,-3.6,-8.3,-13,-49,-67,-67,-71 2.33,.79,1.76,4.40,3.10,2.19,1.71,2.43 8 Sigmoid 0.1794076 ⋯ CENTRAL NERVOUS SYSTEM 0 0 0 0 0 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant
A172_CENTRAL_NERVOUS_SYSTEM A172 PHA-665752 c-MET .0025,.0080,.025,.080,.25,.80,2.53,8 -11,-.73,3.40,1.00,.26,5.42,7.48,10.4 .86,7.72,2.80,14.1,6.64,2.25,7.98,6.82 8 Linear NA ⋯ CENTRAL NERVOUS SYSTEM 0 0 0 0 0 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant
A172_CENTRAL_NERVOUS_SYSTEM A172 Lapatinib EGFR .0025,.0080,.025,.080,.25,.80,2.53,8 3.48,1.18,.20,2.58,-4.6,-2.1,-8.0,-4.2 .91,.27,3.61,4.82,4.66,6.00,3.26,8.81 8 Constant NA ⋯ CENTRAL NERVOUS SYSTEM 0 0 0 0 0 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant
A172_CENTRAL_NERVOUS_SYSTEM A172 Nutlin-3 MDM2 .0025,.0080,.025,.080,.25,.80,2.53,8 -1.7,-6.0,1.23,-2.8,-3.8,-5.6,-11,-28 3.06,6.86,8.66,2.42,8.39,14.8,13.4,9.028 Sigmoid 8.3907196 ⋯ CENTRAL NERVOUS SYSTEM 0 0 0 0 0 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllllllllllllllllllllll}\n", + " X1 & Primary Cell Line Name & Compound & Target & Doses (uM) & Activity Data (median) & Activity SD & Num Data & FitType & EC50 (uM) & ⋯ & tissue & AKT1\\_MUT & PIK3CA\\_MUT & KRAS\\_MUT & ERBB2\\_MUT & BRAF\\_MUT & pi3k\\_status & weight & sample\\_name & predictions\\\\\n", + "\\hline\n", + "\t A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & A172 & AEW541 & IGF1R & .0025,.0080,.025,.080,.25,.80,2.53,8 & -.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-22 & 5.84,1.59,2.77,5.89,2.43,3.25,4.54,7.73 & 8 & Sigmoid & 8.4196215 & ⋯ & CENTRAL NERVOUS SYSTEM & 0 & 0 & 0 & 0 & 0 & 0 & 0.5489741 & A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & mutant \\\\\n", + "\t A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & A172 & Nilotinib & ABL & .0025,.0080,.025,.080,.25,.80,2.53,8 & -.75,3.15,9.45,7.81,1.15,4.57,-3.7,-2.1 & 6.44,3.36,2.34,1.18,1.86,3.21,4.71,6.22 & 8 & Constant & NA & ⋯ & CENTRAL NERVOUS SYSTEM & 0 & 0 & 0 & 0 & 0 & 0 & 0.5489741 & A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & mutant \\\\\n", + "\t A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & A172 & 17-AAG & HSP90 & .0025,.0080,.025,.080,.25,.80,2.53,8 & -4.1,-3.6,-8.3,-13,-49,-67,-67,-71 & 2.33,.79,1.76,4.40,3.10,2.19,1.71,2.43 & 8 & Sigmoid & 0.1794076 & ⋯ & CENTRAL NERVOUS SYSTEM & 0 & 0 & 0 & 0 & 0 & 0 & 0.5489741 & A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & mutant \\\\\n", + "\t A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & A172 & PHA-665752 & c-MET & .0025,.0080,.025,.080,.25,.80,2.53,8 & -11,-.73,3.40,1.00,.26,5.42,7.48,10.4 & .86,7.72,2.80,14.1,6.64,2.25,7.98,6.82 & 8 & Linear & NA & ⋯ & CENTRAL NERVOUS SYSTEM & 0 & 0 & 0 & 0 & 0 & 0 & 0.5489741 & A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & mutant \\\\\n", + "\t A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & A172 & Lapatinib & EGFR & .0025,.0080,.025,.080,.25,.80,2.53,8 & 3.48,1.18,.20,2.58,-4.6,-2.1,-8.0,-4.2 & .91,.27,3.61,4.82,4.66,6.00,3.26,8.81 & 8 & Constant & NA & ⋯ & CENTRAL NERVOUS SYSTEM & 0 & 0 & 0 & 0 & 0 & 0 & 0.5489741 & A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & mutant \\\\\n", + "\t A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & A172 & Nutlin-3 & MDM2 & .0025,.0080,.025,.080,.25,.80,2.53,8 & -1.7,-6.0,1.23,-2.8,-3.8,-5.6,-11,-28 & 3.06,6.86,8.66,2.42,8.39,14.8,13.4,9.02 & 8 & Sigmoid & 8.3907196 & ⋯ & CENTRAL NERVOUS SYSTEM & 0 & 0 & 0 & 0 & 0 & 0 & 0.5489741 & A172\\_CENTRAL\\_NERVOUS\\_SYSTEM & mutant \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "X1 | Primary Cell Line Name | Compound | Target | Doses (uM) | Activity Data (median) | Activity SD | Num Data | FitType | EC50 (uM) | ⋯ | tissue | AKT1_MUT | PIK3CA_MUT | KRAS_MUT | ERBB2_MUT | BRAF_MUT | pi3k_status | weight | sample_name | predictions | \n", + "|---|---|---|---|---|---|\n", + "| A172_CENTRAL_NERVOUS_SYSTEM | A172 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-22 | 5.84,1.59,2.77,5.89,2.43,3.25,4.54,7.73 | 8 | Sigmoid | 8.4196215 | ⋯ | CENTRAL NERVOUS SYSTEM | 0 | 0 | 0 | 0 | 0 | 0 | 0.5489741 | A172_CENTRAL_NERVOUS_SYSTEM | mutant | \n", + "| A172_CENTRAL_NERVOUS_SYSTEM | A172 | Nilotinib | ABL | .0025,.0080,.025,.080,.25,.80,2.53,8 | -.75,3.15,9.45,7.81,1.15,4.57,-3.7,-2.1 | 6.44,3.36,2.34,1.18,1.86,3.21,4.71,6.22 | 8 | Constant | NA | ⋯ | CENTRAL NERVOUS SYSTEM | 0 | 0 | 0 | 0 | 0 | 0 | 0.5489741 | A172_CENTRAL_NERVOUS_SYSTEM | mutant | \n", + "| A172_CENTRAL_NERVOUS_SYSTEM | A172 | 17-AAG | HSP90 | .0025,.0080,.025,.080,.25,.80,2.53,8 | -4.1,-3.6,-8.3,-13,-49,-67,-67,-71 | 2.33,.79,1.76,4.40,3.10,2.19,1.71,2.43 | 8 | Sigmoid | 0.1794076 | ⋯ | CENTRAL NERVOUS SYSTEM | 0 | 0 | 0 | 0 | 0 | 0 | 0.5489741 | A172_CENTRAL_NERVOUS_SYSTEM | mutant | \n", + "| A172_CENTRAL_NERVOUS_SYSTEM | A172 | PHA-665752 | c-MET | .0025,.0080,.025,.080,.25,.80,2.53,8 | -11,-.73,3.40,1.00,.26,5.42,7.48,10.4 | .86,7.72,2.80,14.1,6.64,2.25,7.98,6.82 | 8 | Linear | NA | ⋯ | CENTRAL NERVOUS SYSTEM | 0 | 0 | 0 | 0 | 0 | 0 | 0.5489741 | A172_CENTRAL_NERVOUS_SYSTEM | mutant | \n", + "| A172_CENTRAL_NERVOUS_SYSTEM | A172 | Lapatinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 3.48,1.18,.20,2.58,-4.6,-2.1,-8.0,-4.2 | .91,.27,3.61,4.82,4.66,6.00,3.26,8.81 | 8 | Constant | NA | ⋯ | CENTRAL NERVOUS SYSTEM | 0 | 0 | 0 | 0 | 0 | 0 | 0.5489741 | A172_CENTRAL_NERVOUS_SYSTEM | mutant | \n", + "| A172_CENTRAL_NERVOUS_SYSTEM | A172 | Nutlin-3 | MDM2 | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.7,-6.0,1.23,-2.8,-3.8,-5.6,-11,-28 | 3.06,6.86,8.66,2.42,8.39,14.8,13.4,9.02 | 8 | Sigmoid | 8.3907196 | ⋯ | CENTRAL NERVOUS SYSTEM | 0 | 0 | 0 | 0 | 0 | 0 | 0.5489741 | A172_CENTRAL_NERVOUS_SYSTEM | mutant | \n", + "\n", + "\n" + ], + "text/plain": [ + " X1 Primary Cell Line Name Compound Target\n", + "1 A172_CENTRAL_NERVOUS_SYSTEM A172 AEW541 IGF1R \n", + "2 A172_CENTRAL_NERVOUS_SYSTEM A172 Nilotinib ABL \n", + "3 A172_CENTRAL_NERVOUS_SYSTEM A172 17-AAG HSP90 \n", + "4 A172_CENTRAL_NERVOUS_SYSTEM A172 PHA-665752 c-MET \n", + "5 A172_CENTRAL_NERVOUS_SYSTEM A172 Lapatinib EGFR \n", + "6 A172_CENTRAL_NERVOUS_SYSTEM A172 Nutlin-3 MDM2 \n", + " Doses (uM) Activity Data (median) \n", + "1 .0025,.0080,.025,.080,.25,.80,2.53,8 -.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-22 \n", + "2 .0025,.0080,.025,.080,.25,.80,2.53,8 -.75,3.15,9.45,7.81,1.15,4.57,-3.7,-2.1\n", + "3 .0025,.0080,.025,.080,.25,.80,2.53,8 -4.1,-3.6,-8.3,-13,-49,-67,-67,-71 \n", + "4 .0025,.0080,.025,.080,.25,.80,2.53,8 -11,-.73,3.40,1.00,.26,5.42,7.48,10.4 \n", + "5 .0025,.0080,.025,.080,.25,.80,2.53,8 3.48,1.18,.20,2.58,-4.6,-2.1,-8.0,-4.2 \n", + "6 .0025,.0080,.025,.080,.25,.80,2.53,8 -1.7,-6.0,1.23,-2.8,-3.8,-5.6,-11,-28 \n", + " Activity SD Num Data FitType EC50 (uM) ⋯\n", + "1 5.84,1.59,2.77,5.89,2.43,3.25,4.54,7.73 8 Sigmoid 8.4196215 ⋯\n", + "2 6.44,3.36,2.34,1.18,1.86,3.21,4.71,6.22 8 Constant NA ⋯\n", + "3 2.33,.79,1.76,4.40,3.10,2.19,1.71,2.43 8 Sigmoid 0.1794076 ⋯\n", + "4 .86,7.72,2.80,14.1,6.64,2.25,7.98,6.82 8 Linear NA ⋯\n", + "5 .91,.27,3.61,4.82,4.66,6.00,3.26,8.81 8 Constant NA ⋯\n", + "6 3.06,6.86,8.66,2.42,8.39,14.8,13.4,9.02 8 Sigmoid 8.3907196 ⋯\n", + " tissue AKT1_MUT PIK3CA_MUT KRAS_MUT ERBB2_MUT BRAF_MUT\n", + "1 CENTRAL NERVOUS SYSTEM 0 0 0 0 0 \n", + "2 CENTRAL NERVOUS SYSTEM 0 0 0 0 0 \n", + "3 CENTRAL NERVOUS SYSTEM 0 0 0 0 0 \n", + "4 CENTRAL NERVOUS SYSTEM 0 0 0 0 0 \n", + "5 CENTRAL NERVOUS SYSTEM 0 0 0 0 0 \n", + "6 CENTRAL NERVOUS SYSTEM 0 0 0 0 0 \n", + " pi3k_status weight sample_name predictions\n", + "1 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "2 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "3 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "4 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "5 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "6 0 0.5489741 A172_CENTRAL_NERVOUS_SYSTEM mutant " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pharm_file <- file.path(\"..\",\"data\", \"pharmacology_predictions_ERBB2_PIK3CA_KRAS_AKT1_ccle.tsv\")\n", + "pharm_full_df <- readr::read_tsv(pharm_file)\n", + "head(pharm_full_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
AEW541
Nilotinib
17-AAG
PHA-665752
Lapatinib
Nutlin-3
AZD0530
PF2341066
L-685458
ZD-6474
Panobinostat
Sorafenib
Irinotecan
Topotecan
LBW242
PD-0325901
PD-0332991
Paclitaxel
AZD6244
PLX4720
RAF265
TAE684
TKI258
Erlotinib
\n" + ], + "text/latex": [ + "\\begin{tabular}{l}\n", + "\t AEW541 \\\\\n", + "\t Nilotinib \\\\\n", + "\t 17-AAG \\\\\n", + "\t PHA-665752 \\\\\n", + "\t Lapatinib \\\\\n", + "\t Nutlin-3 \\\\\n", + "\t AZD0530 \\\\\n", + "\t PF2341066 \\\\\n", + "\t L-685458 \\\\\n", + "\t ZD-6474 \\\\\n", + "\t Panobinostat\\\\\n", + "\t Sorafenib \\\\\n", + "\t Irinotecan \\\\\n", + "\t Topotecan \\\\\n", + "\t LBW242 \\\\\n", + "\t PD-0325901 \\\\\n", + "\t PD-0332991 \\\\\n", + "\t Paclitaxel \\\\\n", + "\t AZD6244 \\\\\n", + "\t PLX4720 \\\\\n", + "\t RAF265 \\\\\n", + "\t TAE684 \\\\\n", + "\t TKI258 \\\\\n", + "\t Erlotinib \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| AEW541 | \n", + "| Nilotinib | \n", + "| 17-AAG | \n", + "| PHA-665752 | \n", + "| Lapatinib | \n", + "| Nutlin-3 | \n", + "| AZD0530 | \n", + "| PF2341066 | \n", + "| L-685458 | \n", + "| ZD-6474 | \n", + "| Panobinostat | \n", + "| Sorafenib | \n", + "| Irinotecan | \n", + "| Topotecan | \n", + "| LBW242 | \n", + "| PD-0325901 | \n", + "| PD-0332991 | \n", + "| Paclitaxel | \n", + "| AZD6244 | \n", + "| PLX4720 | \n", + "| RAF265 | \n", + "| TAE684 | \n", + "| TKI258 | \n", + "| Erlotinib | \n", + "\n", + "\n" + ], + "text/plain": [ + " [,1] \n", + " [1,] AEW541 \n", + " [2,] Nilotinib \n", + " [3,] 17-AAG \n", + " [4,] PHA-665752 \n", + " [5,] Lapatinib \n", + " [6,] Nutlin-3 \n", + " [7,] AZD0530 \n", + " [8,] PF2341066 \n", + " [9,] L-685458 \n", + "[10,] ZD-6474 \n", + "[11,] Panobinostat\n", + "[12,] Sorafenib \n", + "[13,] Irinotecan \n", + "[14,] Topotecan \n", + "[15,] LBW242 \n", + "[16,] PD-0325901 \n", + "[17,] PD-0332991 \n", + "[18,] Paclitaxel \n", + "[19,] AZD6244 \n", + "[20,] PLX4720 \n", + "[21,] RAF265 \n", + "[22,] TAE684 \n", + "[23,] TKI258 \n", + "[24,] Erlotinib " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "comp <- as.matrix(pharm_full_df$Compound)\n", + "comp <- unique(comp)\n", + "comp" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "plot_drug <- function(pharm_df, compound, tissues = NULL, include_braf = FALSE, \n", + " facet_tissue = TRUE, se = FALSE) {\n", + " # Output scatter plots with correlations, visualizing drug activity\n", + " # compared to Ras classifier Score\n", + " #\n", + " # Arguments:\n", + " # pharm_df - dataframe of compound activity by cell line with Ras/BRAF status\n", + " # compound - a specific compound to visualize\n", + " # tissues - a list of tissues to consider plotting specifically in facets\n", + " # include_braf - boolean to include BRAF in considering mutation status\n", + " # facet_tissue - boolean of tissues to determine to plot in facet_wrap\n", + " # se - boolean to plot standard error intervals in geom_smooth\n", + " #\n", + " # Output:\n", + " # Scatter plot with correlation information\n", + "\n", + " pharm_subset_df <- pharm_df[pharm_df$Compound == compound, ]\n", + " if (!is.null(tissues)) {\n", + " pharm_subset_df <- pharm_subset_df %>%\n", + " dplyr::filter(tissue %in% focus_tissues)\n", + " }\n", + " if (include_braf) {\n", + " pharm_subset_df$pi3k_status[pharm_subset_df$BRAF_MUT == 1] <- 1\n", + " legend_label <- \"Pi3k/BRAF Status\"\n", + " } else {\n", + " legend_label <- \"pi3k Status\"\n", + " }\n", + " \n", + " if (compound == \"AZD6244\") {\n", + " compound <- \"Selumetinib\"\n", + " }\n", + "\n", + "formula <- y ~ x\n", + " p <- ggplot(pharm_subset_df, aes(x = weight, y = ActArea,\n", + " color = as.factor(pi3k_status),\n", + " fill = as.factor(pi3k_status))) +\n", + " geom_point(alpha = 0.5, size = 2) +\n", + " scale_x_continuous(breaks = c(0, 0.5, 1),\n", + " limits = c(-0.1, 1.1)) +\n", + " geom_smooth(method = \"lm\", se = se) +\n", + " geom_segment(aes(x = 0.5, y = -0.1, xend = 0.5, yend = 6),\n", + " linetype = \"dashed\", color = \"grey\") +\n", + " scale_fill_manual(values = c(\"#377eb8\", \"#ff7f00\"),\n", + " name = legend_label,\n", + " breaks = c(0, 1),\n", + " labels = c(\"Wild-Type\", \"Mutant\")) +\n", + " scale_color_manual(values = c(\"#377eb8\", \"#ff7f00\"),\n", + " name = legend_label,\n", + " breaks = c(0, 1),\n", + " labels = c(\"Wild-Type\", \"Mutant\")) +\n", + " stat_poly_eq(aes(label = paste(..rr.label..)),\n", + " label.x.npc = 0.17, label.y.npc = 0.92,\n", + " formula = formula,\n", + " parse = TRUE, size = 4, na.rm = TRUE,\n", + " rr.digits = 1) +\n", + " stat_fit_glance(method = \"lm\", geom = \"text\",\n", + " label.x.npc = 0.8, label.y.npc = 0.97,\n", + " method.args = list(formula = formula), size = 4,\n", + " aes(label = paste(\"P = \",\n", + " signif(..p.value.., digits = 1),\n", + " sep = \"\"))) +\n", + " xlab(\"pi3k Classifier Score\") +\n", + " ylab(\"Activity Area\") +\n", + " ggtitle(compound, subtitle = \"CCLE Response\") + \n", + " theme_bw() +\n", + " theme(axis.text = element_text(size = 12),\n", + " axis.title = element_text(size = 14),\n", + " plot.title = element_text(hjust = 0.5),\n", + " plot.subtitle = element_text(hjust = 0.5))\n", + " \n", + " if (facet_tissue) {\n", + " p <- p + facet_wrap(\"tissue\")\n", + " }\n", + " \n", + " return(p)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": 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[ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "focus_tissues <- c(\"CENTRAL NERVOUS SYSTEM\", \"SKIN\", \"BREAST\", \"HAEMATOPOIETIC AND LYMPHOID TISSUE\", \"LARGE INTESTINE\",\n", + " \"LUNG\", \"OVARY\", \"PANCREAS\", \"LIVER\")\n", + "\n", + "pdf(\"/data/vijay/git/pancancer/figures/cell_line/pi3k_gain_all_drug_response.pdf\")\n", + "\n", + "sel_fig <- plot_drug(pharm_full_df, \"AZD6244\", facet_tissue = FALSE, se = TRUE)\n", + "sel_fig\n", + "\n", + "p_fig_AEW541 <- plot_drug(pharm_full_df, \"AEW541\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_AEW541\n", + "\n", + "p_fig_Nilotinib <- plot_drug(pharm_full_df, \"Nilotinib\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Nilotinib\n", + "\n", + "p_fig_17 <- plot_drug(pharm_full_df, \"17-AAG\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_17\n", + "\n", + "p_fig_PHA665752 <- plot_drug(pharm_full_df, \"PHA-665752\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_PHA665752\n", + "\n", + "p_fig_Lapatinib <- plot_drug(pharm_full_df, \"Lapatinib\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Lapatinib\n", + "\n", + "p_fig_Nutlin <- plot_drug(pharm_full_df, \"Nutlin-3\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Nutlin\n", + "\n", + "p_fig_AZD0530 <- plot_drug(pharm_full_df, \"AZD0530\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_AZD0530\n", + "\n", + "p_fig_PF2341066 <- plot_drug(pharm_full_df, \"PF2341066\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_PF2341066\n", + "\n", + "p_fig_L685458 <- plot_drug(pharm_full_df, \"L-685458\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_L685458\n", + "\n", + "p_fig_ZD6474 <- plot_drug(pharm_full_df, \"ZD-6474\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_ZD6474\n", + "\n", + "p_fig_Panobinostat <- plot_drug(pharm_full_df, \"Panobinostat\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Panobinostat\n", + "\n", + "p_fig_Sorafenib <- plot_drug(pharm_full_df, \"Sorafenib\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Sorafenib\n", + "\n", + "p_fig_Irinotecan <- plot_drug(pharm_full_df, \"Irinotecan\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Irinotecan\n", + "\n", + "p_fig_Topotecan <- plot_drug(pharm_full_df, \"Topotecan\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Topotecan\n", + "\n", + "p_fig_LBW242 <- plot_drug(pharm_full_df, \"LBW242\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_LBW242\n", + "\n", + "p_fig_PD0325901 <- plot_drug(pharm_full_df, \"PD-0325901\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_PD0325901\n", + "\n", + "p_fig_PD0332991 <- plot_drug(pharm_full_df, \"PD-0332991\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_PD0332991\n", + "\n", + "p_fig_Paclitaxel <- plot_drug(pharm_full_df, \"Paclitaxel\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_Paclitaxel\n", + "\n", + "p_fig_PLX4720 <- plot_drug(pharm_full_df, \"PLX4720\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_PLX4720\n", + "\n", + "p_fig_RAF265 <- plot_drug(pharm_full_df, \"RAF265\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_RAF265\n", + "\n", + "p_fig_TAE684 <- plot_drug(pharm_full_df, \"TAE684\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_TAE684\n", + "\n", + "p_fig_TKI258 <- plot_drug(pharm_full_df, \"TKI258\", facet_tissue = FALSE, se = TRUE)\n", + "p_fig_TKI258\n", + "\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + 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TRUE)\n", + "p_fig_AEW541\n", + "\n", + "p_fig_Nilotinib <- plot_drug(pharm_full_df, \"Nilotinib\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Nilotinib\n", + "\n", + "p_fig_17 <- plot_drug(pharm_full_df, \"17-AAG\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_17\n", + "\n", + "p_fig_PHA665752 <- plot_drug(pharm_full_df, \"PHA-665752\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_PHA665752\n", + "\n", + "p_fig_Lapatinib <- plot_drug(pharm_full_df, \"Lapatinib\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Lapatinib\n", + "\n", + "p_fig_Nutlin <- plot_drug(pharm_full_df, \"Nutlin-3\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Nutlin\n", + "\n", + "p_fig_AZD0530 <- plot_drug(pharm_full_df, \"AZD0530\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_AZD0530\n", + "\n", + "p_fig_PF2341066 <- plot_drug(pharm_full_df, \"PF2341066\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_PF2341066\n", + "\n", + "p_fig_L685458 <- plot_drug(pharm_full_df, \"L-685458\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_L685458\n", + "\n", + "p_fig_ZD6474 <- plot_drug(pharm_full_df, \"ZD-6474\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_ZD6474\n", + "\n", + "p_fig_Panobinostat <- plot_drug(pharm_full_df, \"Panobinostat\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Panobinostat\n", + "\n", + "p_fig_Sorafenib <- plot_drug(pharm_full_df, \"Sorafenib\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Sorafenib\n", + "\n", + "p_fig_Irinotecan <- plot_drug(pharm_full_df, \"Irinotecan\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Irinotecan\n", + "\n", + "p_fig_Topotecan <- plot_drug(pharm_full_df, \"Topotecan\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Topotecan\n", + "\n", + "p_fig_LBW242 <- plot_drug(pharm_full_df, \"LBW242\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_LBW242\n", + "\n", + "p_fig_PD0325901 <- plot_drug(pharm_full_df, \"PD-0325901\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_PD0325901\n", + "\n", + "p_fig_PD0332991 <- plot_drug(pharm_full_df, \"PD-0332991\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_PD0332991\n", + "\n", + "p_fig_Paclitaxel <- plot_drug(pharm_full_df, \"Paclitaxel\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_Paclitaxel\n", + "\n", + "p_fig_PLX4720 <- plot_drug(pharm_full_df, \"PLX4720\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_PLX4720\n", + "\n", + "p_fig_RAF265 <- plot_drug(pharm_full_df, \"RAF265\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_RAF265\n", + "\n", + "p_fig_TAE684 <- plot_drug(pharm_full_df, \"TAE684\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_TAE684\n", + "\n", + "p_fig_TKI258 <- plot_drug(pharm_full_df, \"TKI258\", facet_tissue = FALSE, include_braf = TRUE, se = TRUE)\n", + "p_fig_TKI258\n", + "\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" 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], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "focus_tissues <- c(\"CENTRAL NERVOUS SYSTEM\", \"SKIN\", \"BREAST\", \"HAEMATOPOIETIC AND LYMPHOID TISSUE\", \"LARGE INTESTINE\",\n", + " \"LUNG\", \"OVARY\", \"PANCREAS\", \"LIVER\")\n", + "\n", + "pdf(\"/data/vijay/git/pancancer/figures/cell_line/pi3k_gain_tissue_response.pdf\")\n", + "\n", + "sel_fig <- plot_drug(pharm_full_df, \"AZD6244\", tissues = focus_tissues)\n", + "sel_fig\n", + "\n", + "p_fig_AEW541 <- plot_drug(pharm_full_df, \"AEW541\", tissues = focus_tissues)\n", + "p_fig_AEW541\n", + "\n", + "p_fig_Nilotinib <- plot_drug(pharm_full_df, \"Nilotinib\", tissues = focus_tissues)\n", + "p_fig_Nilotinib\n", + "\n", + "p_fig_17 <- plot_drug(pharm_full_df, \"17-AAG\", tissues = focus_tissues)\n", + "p_fig_17\n", + "\n", + "p_fig_PHA665752 <- plot_drug(pharm_full_df, \"PHA-665752\", tissues = focus_tissues)\n", + "p_fig_PHA665752\n", + "\n", + "p_fig_Lapatinib <- plot_drug(pharm_full_df, \"Lapatinib\", tissues = focus_tissues)\n", + "p_fig_Lapatinib\n", + "\n", + "p_fig_Nutlin <- plot_drug(pharm_full_df, \"Nutlin-3\", tissues = focus_tissues)\n", + "p_fig_Nutlin\n", + "\n", + "p_fig_AZD0530 <- plot_drug(pharm_full_df, \"AZD0530\", tissues = focus_tissues)\n", + "p_fig_AZD0530\n", + "\n", + "p_fig_PF2341066 <- plot_drug(pharm_full_df, \"PF2341066\", tissues = focus_tissues)\n", + "p_fig_PF2341066\n", + "\n", + "p_fig_L685458 <- plot_drug(pharm_full_df, \"L-685458\", tissues = focus_tissues)\n", + "p_fig_L685458\n", + "\n", + "p_fig_ZD6474 <- plot_drug(pharm_full_df, \"ZD-6474\", tissues = focus_tissues)\n", + "p_fig_ZD6474\n", + "\n", + "p_fig_Panobinostat <- plot_drug(pharm_full_df, \"Panobinostat\", tissues = focus_tissues)\n", + "p_fig_Panobinostat\n", + "\n", + "p_fig_Sorafenib <- plot_drug(pharm_full_df, \"Sorafenib\", tissues = focus_tissues)\n", + "p_fig_Sorafenib\n", + "\n", + "p_fig_Irinotecan <- plot_drug(pharm_full_df, \"Irinotecan\", tissues = focus_tissues)\n", + "p_fig_Irinotecan\n", + "\n", + "p_fig_Topotecan <- plot_drug(pharm_full_df, \"Topotecan\", tissues = focus_tissues)\n", + "p_fig_Topotecan\n", + "\n", + "p_fig_LBW242 <- plot_drug(pharm_full_df, \"LBW242\", tissues = focus_tissues)\n", + "p_fig_LBW242\n", + "\n", + "p_fig_PD0325901 <- plot_drug(pharm_full_df, \"PD-0325901\", tissues = focus_tissues)\n", + "p_fig_PD0325901\n", + "\n", + "p_fig_PD0332991 <- plot_drug(pharm_full_df, \"PD-0332991\", tissues = focus_tissues)\n", + "p_fig_PD0332991\n", + "\n", + "p_fig_Paclitaxel <- plot_drug(pharm_full_df, \"Paclitaxel\", tissues = focus_tissues)\n", + "p_fig_Paclitaxel\n", + "\n", + "p_fig_PLX4720 <- plot_drug(pharm_full_df, \"PLX4720\", tissues = focus_tissues)\n", + "p_fig_PLX4720\n", + "\n", + "p_fig_RAF265 <- plot_drug(pharm_full_df, \"RAF265\", tissues = focus_tissues)\n", + "p_fig_RAF265\n", + "\n", + "p_fig_TAE684 <- plot_drug(pharm_full_df, \"TAE684\", tissues = focus_tissues)\n", + "p_fig_TAE684\n", + "\n", + "p_fig_TKI258 <- plot_drug(pharm_full_df, \"TKI258\", tissues = focus_tissues)\n", + "p_fig_TKI258\n", + "\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "png: 2" + ], + "text/latex": [ + "\\textbf{png:} 2" + ], + "text/markdown": [ + "**png:** 2" + ], + "text/plain": [ + "png \n", + " 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "focus_tissues <- c(\"CENTRAL NERVOUS SYSTEM\", \"SKIN\", \"BREAST\", \"HAEMATOPOIETIC AND LYMPHOID TISSUE\", \"LARGE INTESTINE\",\n", + " \"LUNG\", \"OVARY\", \"PANCREAS\", \"LIVER\")\n", + "\n", + "pdf(\"/data/vijay/git/pancancer/figures/cell_line/pi3k_gain_braf_tissue_response.pdf\")\n", + "\n", + "sel_fig <- plot_drug(pharm_full_df, \"AZD6244\", tissues = focus_tissues)\n", + "sel_fig\n", + "\n", + "p_fig_AEW541 <- plot_drug(pharm_full_df, \"AEW541\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_AEW541\n", + "\n", + "p_fig_Nilotinib <- plot_drug(pharm_full_df, \"Nilotinib\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Nilotinib\n", + "\n", + "p_fig_17 <- plot_drug(pharm_full_df, \"17-AAG\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_17\n", + "\n", + "p_fig_PHA665752 <- plot_drug(pharm_full_df, \"PHA-665752\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_PHA665752\n", + "\n", + "p_fig_Lapatinib <- plot_drug(pharm_full_df, \"Lapatinib\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Lapatinib\n", + "\n", + "p_fig_Nutlin <- plot_drug(pharm_full_df, \"Nutlin-3\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Nutlin\n", + "\n", + "p_fig_AZD0530 <- plot_drug(pharm_full_df, \"AZD0530\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_AZD0530\n", + "\n", + "p_fig_PF2341066 <- plot_drug(pharm_full_df, \"PF2341066\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_PF2341066\n", + "\n", + "p_fig_L685458 <- plot_drug(pharm_full_df, \"L-685458\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_L685458\n", + "\n", + "p_fig_ZD6474 <- plot_drug(pharm_full_df, \"ZD-6474\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_ZD6474\n", + "\n", + "p_fig_Panobinostat <- plot_drug(pharm_full_df, \"Panobinostat\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Panobinostat\n", + "\n", + "p_fig_Sorafenib <- plot_drug(pharm_full_df, \"Sorafenib\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Sorafenib\n", + "\n", + "p_fig_Irinotecan <- plot_drug(pharm_full_df, \"Irinotecan\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Irinotecan\n", + "\n", + "p_fig_Topotecan <- plot_drug(pharm_full_df, \"Topotecan\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Topotecan\n", + "\n", + "p_fig_LBW242 <- plot_drug(pharm_full_df, \"LBW242\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_LBW242\n", + "\n", + "p_fig_PD0325901 <- plot_drug(pharm_full_df, \"PD-0325901\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_PD0325901\n", + "\n", + "p_fig_PD0332991 <- plot_drug(pharm_full_df, \"PD-0332991\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_PD0332991\n", + "\n", + "p_fig_Paclitaxel <- plot_drug(pharm_full_df, \"Paclitaxel\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_Paclitaxel\n", + "\n", + "p_fig_PLX4720 <- plot_drug(pharm_full_df, \"PLX4720\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_PLX4720\n", + "\n", + "p_fig_RAF265 <- plot_drug(pharm_full_df, \"RAF265\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_RAF265\n", + "\n", + "p_fig_TAE684 <- plot_drug(pharm_full_df, \"TAE684\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_TAE684\n", + "\n", + "p_fig_TKI258 <- plot_drug(pharm_full_df, \"TKI258\", include_braf = TRUE, tissues = focus_tissues)\n", + "p_fig_TKI258\n", + "\n", + "dev.off()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": 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R4f0eYfg1va/3t4aKsFf+tgn439txMGe7ciq+2hFXu6x4a9eU9Ctac6k9l6/7Jd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K6wf5CqFb4QlyJAFdOzUlNHms2uENcgmJ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\nS6oXY/jp+fl3rxT2dgaarR7zVe3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Qp1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aho5IpemEV3QcEkIX687qNRPVCdDS16K6FsyBmoDR76K5o8mOrUmEAFwAEaAHOBkufZ/G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6v2Bfm6Wb6i+0R28ZPf7QzycfvqwW6RQd72LaO4zPDJurSkozkXistjwvxHJIb3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\nxvH3PmtWAAASq0lEQVRWU1M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er06dN9+vSheyPqcfjw4fHjxxs8vsbsWD3M/HoLcDiclJQUI39fJgW8kcF84sSJ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\nLMvexUF5lgWEGOaGFcMQ4rib7nAkXvjnI0OPhcj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EoNQjP7ApeF99oLXS47bxHw3E3zg7sbXcPZNqu+aMog4AACAASURBVNzCb5+fdErc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\n9+OD47gB2WQ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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "focus_tissues <- c(\"CENTRAL NERVOUS SYSTEM\", \"SKIN\", \"BREAST\", \"HAEMATOPOIETIC AND LYMPHOID TISSUE\", \"LARGE INTESTINE\",\n", + " \"LUNG\", \"OVARY\", \"PANCREAS\", \"LIVER\")\n", + "\n", + "#Plot and output figures\n", + "# Selumetinib all tissues - Ras only\n", + "sel_fig <- plot_drug(pharm_full_df, \"AZD6244\", facet_tissue = FALSE, se = TRUE)\n", + "#ggplot2::ggsave(selum_file, plot = sel_fig, dpi = 600, height = 4, width = 5.25)\n", + "# Selumetinib all tissues - Ras and BRAF\n", + "sel_braf_fig <- plot_drug(pharm_full_df, \"AZD6244\", facet_tissue = FALSE,\n", + " include_braf = TRUE, se = TRUE)\n", + "#ggplot2::ggsave(selum_braf_file, plot = sel_braf_fig, dpi = 600, height = 4,\n", + "# width = 5.25)\n", + "\n", + "# PD-0325901 all tissues - Ras only\n", + "p_fig <- plot_drug(pharm_full_df, \"Lapatinib\", facet_tissue = FALSE, se = TRUE)\n", + "#ggplot2::ggsave(pd_file, plot = p_fig, dpi = 600, height = 4,\n", + "# width = 5.25)\n", + "p_fig\n", + "# PD-0325901 all tissues - Ras and BRAF\n", + "p_braf_fig <- plot_drug(pharm_full_df, \"Lapatinib\", facet_tissue = FALSE,\n", + " include_braf = TRUE, se = TRUE)\n", + "p_braf_fig\n", + "#ggplot2::ggsave(pd_braf_file, plot = p_braf_fig, dpi = 600,\n", + "# height = 4.5, width = 8)\n", + "# Selumetinib stratified by tissue - Ras only\n", + "sel_tis_fig <- plot_drug(pharm_full_df, \"AZD6244\", tissues = focus_tissues)\n", + "#ggplot2::ggsave(selum_tissue_file, plot = sel_tis_fig, dpi = 600,\n", + "# height = 4.5, width = 8)\n", + "# Selumetinib stratified by tissue - Ras and BRAF\n", + "sel_tis_braf_fig <- plot_drug(pharm_full_df, \"AZD6244\",\n", + " tissues = focus_tissues, include_braf = TRUE)\n", + "#ggplot2::ggsave(selum_tissue_braf_file, plot = sel_tis_braf_fig, dpi = 600,\n", + "# height = 4.5, width = 8)\n", + "\n", + "# PD-0325901 stratified by tissue - Ras only\n", + "p_tis_fig <- plot_drug(pharm_full_df, \"Lapatinib\", tissues = focus_tissues)\n", + "#ggplot2::ggsave(pd_tissue_file, plot = p_tis_fig, dpi = 600,\n", + "# height = 4.5, width = 8)\n", + "p_tis_fig\n", + "# PD-0325901 stratified by tissue - Ras and BRAF\n", + "p_tis_braf_fig <- plot_drug(pharm_full_df, \"PD-0325901\",\n", + " tissues = focus_tissues, include_braf = TRUE)\n", + "#ggplot2::ggsave(pd_tissue_braf_file, plot = p_tis_braf_fig,\n", + "# dpi = 600, height = 4.5, width = 8)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Output Files\n", + "base_path <- file.path(\"figures\", \"cell_line\")\n", + "selum_file <- file.path(base_path, \"selumetinib.pdf\")\n", + "selum_braf_file <- file.path(base_path, \"selumetinib_braf.pdf\")\n", + "selum_tissue_file <- file.path(base_path, \"selumetinib_tissues.pdf\")\n", + "selum_tissue_braf_file <- file.path(base_path, \"selumetinib_tissues_braf.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pd_file <- file.path(base_path, \"pd_0325901.pdf\")\n", + "pd_braf_file <- file.path(base_path, \"pd_0325901_braf.pdf\")\n", + "pd_tissue_file <- file.path(base_path, \"pd_0325901_tissues.pdf\")\n", + "pd_tissue_braf_file <- file.path(base_path, \"pd_0325901_tissues_braf.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R [conda env:pancancer]", + "language": "R", + "name": "conda-env-pancancer-r" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/pi3k_cell_line_predictions.ipynb b/scripts/pi3k_cell_line_predictions.ipynb new file mode 100644 index 0000000..1002130 --- /dev/null +++ b/scripts/pi3k_cell_line_predictions.ipynb @@ -0,0 +1,4077 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cell Line Analysis\n", + "\n", + "We sought to validate the Ras classifier trained on TCGA pan-cancer data by generating predictions on cell line data. A good classifier should generalize to predicting Ras status in other samples. We apply the classifier on two datasets:\n", + "\n", + "1. [GSE94937](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94937) from [Kim et al. 2017](http://doi.org/10.1016/j.cels.2017.08.002)\n", + " * Illumina NextSeq 5000 of Human Small Airway Epithelial Cells expressing KRAS G12V and wild-type KRAS\n", + "2. [Cancer Cell Line Encyclopedia (CCLE)](https://software.broadinstitute.org/software/cprg/?q=node/11) Gene Expression data.\n", + " * 737 cell lines with matching gene expression and mutation calls\n", + " * Pharmacologic profiling of 24 drugs over 504 cell lines\n", + " \n", + "These data were accessed via publicly available resources with help from links in the [UCSD-CCAL Onco-GPS github repository](https://github.com/UCSD-CCAL/onco-gps-paper-analysis)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "from decimal import Decimal\n", + "from scipy.stats import ttest_ind\n", + "from statsmodels.stats.proportion import proportions_chisquare\n", + "from sklearn.preprocessing import StandardScaler\n", + "from Bio.SeqUtils import IUPACData\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotnine as gg" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Store protein change dictionary\n", + "aa = IUPACData.protein_letters_1to3_extended" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load PI3K_gain Classifier Coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(301, 3)\n" + ] + }, + { + "data": { + "text/html": [ + "
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featureweightabs
5497MFN10.0961370.096137
2031PIK3R30.0876890.087689
344LASP10.0876700.087670
3994GRB70.0744770.074477
3884PGAP30.0700230.070023
6757NAAA-0.0676620.067662
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5244STARD30.0646210.064621
3172MRPS350.0634820.063482
3724C17orf370.0604510.060451
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" + ], + "text/plain": [ + " feature weight abs\n", + "5497 MFN1 0.096137 0.096137\n", + "2031 PIK3R3 0.087689 0.087689\n", + "344 LASP1 0.087670 0.087670\n", + "3994 GRB7 0.074477 0.074477\n", + "3884 PGAP3 0.070023 0.070023\n", + "6757 NAAA -0.067662 0.067662\n", + "2793 CABC1 -0.064753 0.064753\n", + "5244 STARD3 0.064621 0.064621\n", + "3172 MRPS35 0.063482 0.063482\n", + "3724 C17orf37 0.060451 0.060451" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier_file = os.path.join('..', 'classifiers', 'ERBB2_PIK3CA_KRAS_AKT1', 'classifier_summary.txt')\n", + "with open(classifier_file) as class_fh:\n", + " for line in class_fh:\n", + " line = line.strip().split('\\t')\n", + " if line[0] == 'Coefficients:':\n", + " all_coef_df = pd.read_table(os.path.join('..', line[1]), index_col=0)\n", + "\n", + "# Only non-zero coefficients contribute to model performance\n", + "coef_df = all_coef_df[all_coef_df['abs'] > 0]\n", + "print(coef_df.shape)\n", + "coef_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2: CCLE\n", + "\n", + "Note - This data was also retrieved from the Onco-GPS paper analysis repository" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(56202, 1020)\n" + ] + }, + { + "data": { + "text/html": [ + "
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Description22RV1_PROSTATE2313287_STOMACH253JBV_URINARY_TRACT253J_URINARY_TRACT42MGBA_CENTRAL_NERVOUS_SYSTEM5637_URINARY_TRACT59M_OVARY639V_URINARY_TRACT647V_URINARY_TRACT...WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEYAPC_PANCREASYD10B_UPPER_AERODIGESTIVE_TRACTYD15_SALIVARY_GLANDYD38_UPPER_AERODIGESTIVE_TRACTYD8_UPPER_AERODIGESTIVE_TRACTYH13_CENTRAL_NERVOUS_SYSTEMYKG1_CENTRAL_NERVOUS_SYSTEMZR751_BREASTZR7530_BREAST
Name
DDX11L1DDX11L10.0534480.0430780.0379560.0283130.0093770.0040120.0174520.0136080.018103...0.1801190.1028980.3547000.4189450.2708190.0964120.1168330.0254810.1241220.030062
WASH7PWASH7P5.7525604.2610403.1004803.0790903.9949403.3567502.5694103.0254902.787330...5.4203602.3235805.6665702.7955102.4327703.7037603.1632703.9835404.6013204.563500
MIR1302-11MIR1302-110.0395760.0119610.0316180.0314460.0208310.0534760.0290750.0403050.040212...0.2445100.1142860.4727480.5726920.4812680.0917850.0707800.0113200.1272550.055648
FAM138AFAM138A0.0374000.0226080.0132800.0264160.0065620.0224610.0183180.0063480.012667...0.1960600.1645780.3847140.2856410.3638530.0771020.1040510.0000000.1336230.028048
OR4G4POR4G4P0.0000000.0194010.0256420.0085010.0084470.0000000.0078600.0163430.000000...0.1442130.0397220.2396190.1354620.0975750.0372180.0956690.0367220.0516010.018052
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5 rows × 1020 columns

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" + ], + "text/plain": [ + " Description 22RV1_PROSTATE 2313287_STOMACH 253JBV_URINARY_TRACT \\\n", + "Name \n", + "DDX11L1 DDX11L1 0.053448 0.043078 0.037956 \n", + "WASH7P WASH7P 5.752560 4.261040 3.100480 \n", + "MIR1302-11 MIR1302-11 0.039576 0.011961 0.031618 \n", + "FAM138A FAM138A 0.037400 0.022608 0.013280 \n", + "OR4G4P OR4G4P 0.000000 0.019401 0.025642 \n", + "\n", + " 253J_URINARY_TRACT 42MGBA_CENTRAL_NERVOUS_SYSTEM \\\n", + "Name \n", + "DDX11L1 0.028313 0.009377 \n", + "WASH7P 3.079090 3.994940 \n", + "MIR1302-11 0.031446 0.020831 \n", + "FAM138A 0.026416 0.006562 \n", + "OR4G4P 0.008501 0.008447 \n", + "\n", + " 5637_URINARY_TRACT 59M_OVARY 639V_URINARY_TRACT \\\n", + "Name \n", + "DDX11L1 0.004012 0.017452 0.013608 \n", + "WASH7P 3.356750 2.569410 3.025490 \n", + "MIR1302-11 0.053476 0.029075 0.040305 \n", + "FAM138A 0.022461 0.018318 0.006348 \n", + "OR4G4P 0.000000 0.007860 0.016343 \n", + "\n", + " 647V_URINARY_TRACT ... \\\n", + "Name ... \n", + "DDX11L1 0.018103 ... \n", + "WASH7P 2.787330 ... \n", + "MIR1302-11 0.040212 ... \n", + "FAM138A 0.012667 ... \n", + "OR4G4P 0.000000 ... \n", + "\n", + " WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE YAPC_PANCREAS \\\n", + "Name \n", + "DDX11L1 0.180119 0.102898 \n", + "WASH7P 5.420360 2.323580 \n", + "MIR1302-11 0.244510 0.114286 \n", + "FAM138A 0.196060 0.164578 \n", + "OR4G4P 0.144213 0.039722 \n", + "\n", + " YD10B_UPPER_AERODIGESTIVE_TRACT YD15_SALIVARY_GLAND \\\n", + "Name \n", + "DDX11L1 0.354700 0.418945 \n", + "WASH7P 5.666570 2.795510 \n", + "MIR1302-11 0.472748 0.572692 \n", + "FAM138A 0.384714 0.285641 \n", + "OR4G4P 0.239619 0.135462 \n", + "\n", + " YD38_UPPER_AERODIGESTIVE_TRACT YD8_UPPER_AERODIGESTIVE_TRACT \\\n", + "Name \n", + "DDX11L1 0.270819 0.096412 \n", + "WASH7P 2.432770 3.703760 \n", + "MIR1302-11 0.481268 0.091785 \n", + "FAM138A 0.363853 0.077102 \n", + "OR4G4P 0.097575 0.037218 \n", + "\n", + " YH13_CENTRAL_NERVOUS_SYSTEM YKG1_CENTRAL_NERVOUS_SYSTEM \\\n", + "Name \n", + "DDX11L1 0.116833 0.025481 \n", + "WASH7P 3.163270 3.983540 \n", + "MIR1302-11 0.070780 0.011320 \n", + "FAM138A 0.104051 0.000000 \n", + "OR4G4P 0.095669 0.036722 \n", + "\n", + " ZR751_BREAST ZR7530_BREAST \n", + "Name \n", + "DDX11L1 0.124122 0.030062 \n", + "WASH7P 4.601320 4.563500 \n", + "MIR1302-11 0.127255 0.055648 \n", + "FAM138A 0.133623 0.028048 \n", + "OR4G4P 0.051601 0.018052 \n", + "\n", + "[5 rows x 1020 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#ccle_file_name = os.path.join('..', '..', 'onco-gps-paper-analysis', 'data',\n", + "# 'rpkm__gene_x_ccle_cellline.gct')\n", + "ccle_file_name = os.path.join('..','data','ccle_rnaseq_genes_rpkm_20180929_mod.gct')\n", + "ccle_df = pd.read_csv(ccle_file_name, skiprows=2, index_col=0)\n", + "print(ccle_df.shape)\n", + "ccle_df = ccle_df.drop_duplicates(subset='Description',keep = 'first')\n", + "ccle_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(289, 3)\n" + ] + }, + { + "data": { + "text/html": [ + "
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featureweightabs
5497MFN10.0961370.096137
2031PIK3R30.0876890.087689
344LASP10.0876700.087670
3994GRB70.0744770.074477
3884PGAP30.0700230.070023
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" + ], + "text/plain": [ + " feature weight abs\n", + "5497 MFN1 0.096137 0.096137\n", + "2031 PIK3R3 0.087689 0.087689\n", + "344 LASP1 0.087670 0.087670\n", + "3994 GRB7 0.074477 0.074477\n", + "3884 PGAP3 0.070023 0.070023" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Subset to common genes in the classifier and CCLE data\n", + "common_genes = list(set(coef_df['feature']) & set(ccle_df.index))\n", + "common_ccle_coef = coef_df[coef_df['feature'].isin(common_genes)]\n", + "print(common_ccle_coef.shape)\n", + "common_ccle_coef.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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22RV1_PROSTATE2313287_STOMACH253JBV_URINARY_TRACT253J_URINARY_TRACT42MGBA_CENTRAL_NERVOUS_SYSTEM5637_URINARY_TRACT59M_OVARY639V_URINARY_TRACT647V_URINARY_TRACT697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE...WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEYAPC_PANCREASYD10B_UPPER_AERODIGESTIVE_TRACTYD15_SALIVARY_GLANDYD38_UPPER_AERODIGESTIVE_TRACTYD8_UPPER_AERODIGESTIVE_TRACTYH13_CENTRAL_NERVOUS_SYSTEMYKG1_CENTRAL_NERVOUS_SYSTEMZR751_BREASTZR7530_BREAST
Name
MFN117.9799011.793709.802157.983147.0006806.934268.30210013.77280010.377806.844000...9.07828011.4441016.6733013.3006010.8186009.51412014.19120012.86860023.366514.3656
PIK3R39.258434.835081.698424.504868.5601802.738001.0054704.9912703.8973012.442100...0.7771153.321732.068001.014422.0509207.3054206.65975012.24540023.944932.9145
LASP143.0720039.8953053.4411031.9988097.803600147.4010095.27480031.85420097.7067023.216100...44.435200139.3060036.1678085.3090037.04520093.12160077.68380047.73310028.4888377.2450
GRB79.3753717.4775016.9279011.134100.0757939.826220.0931990.11522811.276800.056547...0.4737208.023993.803742.6481414.1467000.8627480.5089460.05590110.7048218.0380
PGAP35.767023.261993.452123.911961.6463002.649532.4145702.1828105.782472.368490...3.96582010.249804.819442.599410.5911444.0882404.01619010.52270010.0363323.4960
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253JBV_URINARY_TRACT-0.521264-0.584114-0.2144630.129605-0.182723-0.241336-0.134833-0.127824-0.006052-1.152805...0.671415-0.7906400.147569-0.5869440.492446-0.398434-0.177832-0.761498-0.716895-0.760644
253J_URINARY_TRACT-0.866975-0.193788-0.7081090.007134-0.169896-0.562578-0.161178-0.213826-0.867280-0.988579...0.9079650.412313-0.193082-0.191303-0.5141910.198915-0.461181-1.018329-0.598537-0.397928
42MGBA_CENTRAL_NERVOUS_SYSTEM-1.0536970.3702360.806854-0.226620-0.2330930.080229-0.211802-0.733054-1.125867-0.983712...-0.6137380.2460110.895663-0.783933-0.453879-0.549486-1.292309-0.357339-0.729530-0.509043
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5 rows × 289 columns

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" + ], + "text/plain": [ + "Name MFN1 PIK3R3 LASP1 GRB7 \\\n", + "22RV1_PROSTATE 1.032956 0.467350 -0.453181 -0.030043 \n", + "2313287_STOMACH -0.142760 -0.147860 -0.526315 0.141222 \n", + "253JBV_URINARY_TRACT -0.521264 -0.584114 -0.214463 0.129605 \n", + "253J_URINARY_TRACT -0.866975 -0.193788 -0.708109 0.007134 \n", + "42MGBA_CENTRAL_NERVOUS_SYSTEM -1.053697 0.370236 0.806854 -0.226620 \n", + "\n", + "Name PGAP3 NAAA STARD3 MRPS35 \\\n", + "22RV1_PROSTATE -0.118152 2.198144 -0.270842 2.173606 \n", + "2313287_STOMACH -0.188026 -0.900891 -0.334505 1.036183 \n", + "253JBV_URINARY_TRACT -0.182723 -0.241336 -0.134833 -0.127824 \n", + "253J_URINARY_TRACT -0.169896 -0.562578 -0.161178 -0.213826 \n", + "42MGBA_CENTRAL_NERVOUS_SYSTEM -0.233093 0.080229 -0.211802 -0.733054 \n", + "\n", + "Name ACTL6A TTC17 ... TMEM164 \\\n", + "22RV1_PROSTATE -0.061261 0.372871 ... -0.047132 \n", + "2313287_STOMACH -0.434714 0.755835 ... 2.883026 \n", + "253JBV_URINARY_TRACT -0.006052 -1.152805 ... 0.671415 \n", + "253J_URINARY_TRACT -0.867280 -0.988579 ... 0.907965 \n", + "42MGBA_CENTRAL_NERVOUS_SYSTEM -1.125867 -0.983712 ... -0.613738 \n", + "\n", + "Name FAM32A SRRM1 INSR PPP1R3B \\\n", + "22RV1_PROSTATE -0.990447 -0.289350 0.295792 -0.320896 \n", + "2313287_STOMACH -1.138328 -0.151228 -0.253229 -0.624047 \n", + "253JBV_URINARY_TRACT -0.790640 0.147569 -0.586944 0.492446 \n", + "253J_URINARY_TRACT 0.412313 -0.193082 -0.191303 -0.514191 \n", + "42MGBA_CENTRAL_NERVOUS_SYSTEM 0.246011 0.895663 -0.783933 -0.453879 \n", + "\n", + "Name MGAT4A EBPL DDHD2 ARHGAP8 \\\n", + "22RV1_PROSTATE -0.325682 0.263228 0.287485 0.354803 \n", + "2313287_STOMACH -0.352718 0.547344 0.388898 0.217034 \n", + "253JBV_URINARY_TRACT -0.398434 -0.177832 -0.761498 -0.716895 \n", + "253J_URINARY_TRACT 0.198915 -0.461181 -1.018329 -0.598537 \n", + "42MGBA_CENTRAL_NERVOUS_SYSTEM -0.549486 -1.292309 -0.357339 -0.729530 \n", + "\n", + "Name ARHGAP12 \n", + "22RV1_PROSTATE -0.292374 \n", + "2313287_STOMACH 4.631226 \n", + "253JBV_URINARY_TRACT -0.760644 \n", + "253J_URINARY_TRACT -0.397928 \n", + "42MGBA_CENTRAL_NERVOUS_SYSTEM -0.509043 \n", + "\n", + "[5 rows x 289 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaled_fit = StandardScaler().fit(ccle_df.T)\n", + "ccle_df = pd.DataFrame(scaled_fit.transform(ccle_df.T),\n", + " index=ccle_df.columns,\n", + " columns=ccle_df.index)\n", + "\n", + "print(ccle_df.shape)\n", + "ccle_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(289, 1019)\n" + ] + }, + { + "data": { + "text/html": [ + "
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22RV1_PROSTATE2313287_STOMACH253JBV_URINARY_TRACT253J_URINARY_TRACT42MGBA_CENTRAL_NERVOUS_SYSTEM5637_URINARY_TRACT59M_OVARY639V_URINARY_TRACT647V_URINARY_TRACT697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE...WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEYAPC_PANCREASYD10B_UPPER_AERODIGESTIVE_TRACTYD15_SALIVARY_GLANDYD38_UPPER_AERODIGESTIVE_TRACTYD8_UPPER_AERODIGESTIVE_TRACTYH13_CENTRAL_NERVOUS_SYSTEMYKG1_CENTRAL_NERVOUS_SYSTEMZR751_BREASTZR7530_BREAST
Name
MFN11.032956-0.142760-0.521264-0.866975-1.053697-1.066320-0.8063550.233377-0.411859-1.083474...-0.658839-0.2092030.7846310.143633-0.328083-0.5760050.3128960.0615302.0567050.346041
PIK3R30.467350-0.147860-0.584114-0.1937880.370236-0.439526-0.680491-0.126136-0.2782880.910142...-0.712251-0.358340-0.532712-0.679246-0.5350870.1957210.1059200.8827842.5099783.757490
LASP1-0.453181-0.526315-0.214463-0.7081090.8068541.9486880.748635-0.7114380.804623-0.910305...-0.4217981.762325-0.6121300.519202-0.5919310.6990640.343654-0.345873-0.7889177.240173
GRB7-0.0300430.1412220.1296050.007134-0.226620-0.020513-0.226252-0.2257870.010150-0.227027...-0.218209-0.058609-0.147818-0.1722450.070815-0.209985-0.217464-0.227041-0.0019414.380733
PGAP3-0.118152-0.188026-0.182723-0.169896-0.233093-0.205110-0.211663-0.218128-0.117721-0.212949...-0.1683940.006888-0.144583-0.206508-0.262525-0.164979-0.1669890.0145000.0009338.744406
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5 rows × 1019 columns

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" + ], + "text/plain": [ + " weight\n", + "feature \n", + "MFN1 0.096137\n", + "PIK3R3 0.087689\n", + "LASP1 0.087670\n", + "GRB7 0.074477\n", + "PGAP3 0.070023" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the weights ready for applying the classifier\n", + "apply_weights = pd.DataFrame(common_ccle_coef['weight'])\n", + "apply_weights.index = common_ccle_coef.feature\n", + "print(apply_weights.shape)\n", + "apply_weights.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply a logit transform [y = 1/(1+e^(-wX))] to output probabilities\n", + "result_ccle = apply_weights.T.dot(ccle_df)\n", + "result_ccle = 1 / (1 + np.exp(-1 * result_ccle))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DescriptionDMS53_LUNGSW1116_LARGE_INTESTINENCIH1694_LUNGP3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEHUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEUMUC3_URINARY_TRACTHOS_BONEHUNS1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEAML193_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE...PLB985_HAEMATOPOIETIC_AND_LYMPHOID_TISSUESKNO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUEHOP62_LUNGEKVX_LUNGOVCAR5_OVARYUO31_KIDNEYSF268_CENTRAL_NERVOUS_SYSTEMSF539_CENTRAL_NERVOUS_SYSTEMSNB75_CENTRAL_NERVOUS_SYSTEMHOP92_LUNG
Name
AKT3_MUTAKT3_MUT000000000...0000000000
ABI1_MUTABI1_MUT000001000...0000000000
CDH2_MUTCDH2_MUT000000000...0000000000
LOC100130776_MUTLOC100130776_MUT000000000...0000000000
HDAC6_MUTHDAC6_MUT000000000...0000000000
\n", + "

5 rows × 1031 columns

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" + ], + "text/plain": [ + " Description DMS53_LUNG SW1116_LARGE_INTESTINE \\\n", + "Name \n", + "AKT3_MUT AKT3_MUT 0 0 \n", + "ABI1_MUT ABI1_MUT 0 0 \n", + "CDH2_MUT CDH2_MUT 0 0 \n", + "LOC100130776_MUT LOC100130776_MUT 0 0 \n", + "HDAC6_MUT HDAC6_MUT 0 0 \n", + "\n", + " NCIH1694_LUNG P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE \\\n", + "Name \n", + "AKT3_MUT 0 0 \n", + "ABI1_MUT 0 0 \n", + "CDH2_MUT 0 0 \n", + "LOC100130776_MUT 0 0 \n", + "HDAC6_MUT 0 0 \n", + "\n", + " HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE \\\n", + "Name \n", + "AKT3_MUT 0 \n", + "ABI1_MUT 0 \n", + "CDH2_MUT 0 \n", + "LOC100130776_MUT 0 \n", + "HDAC6_MUT 0 \n", + "\n", + " UMUC3_URINARY_TRACT HOS_BONE \\\n", + "Name \n", + "AKT3_MUT 0 0 \n", + "ABI1_MUT 1 0 \n", + "CDH2_MUT 0 0 \n", + "LOC100130776_MUT 0 0 \n", + "HDAC6_MUT 0 0 \n", + "\n", + " HUNS1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE \\\n", + "Name \n", + "AKT3_MUT 0 \n", + "ABI1_MUT 0 \n", + "CDH2_MUT 0 \n", + "LOC100130776_MUT 0 \n", + "HDAC6_MUT 0 \n", + "\n", + " AML193_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE ... \\\n", + "Name ... \n", + "AKT3_MUT 0 ... \n", + "ABI1_MUT 0 ... \n", + "CDH2_MUT 0 ... \n", + "LOC100130776_MUT 0 ... \n", + "HDAC6_MUT 0 ... \n", + "\n", + " PLB985_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE \\\n", + "Name \n", + "AKT3_MUT 0 \n", + "ABI1_MUT 0 \n", + "CDH2_MUT 0 \n", + "LOC100130776_MUT 0 \n", + "HDAC6_MUT 0 \n", + "\n", + " SKNO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE HOP62_LUNG \\\n", + "Name \n", + "AKT3_MUT 0 0 \n", + "ABI1_MUT 0 0 \n", + "CDH2_MUT 0 0 \n", + "LOC100130776_MUT 0 0 \n", + "HDAC6_MUT 0 0 \n", + "\n", + " EKVX_LUNG OVCAR5_OVARY UO31_KIDNEY \\\n", + "Name \n", + "AKT3_MUT 0 0 0 \n", + "ABI1_MUT 0 0 0 \n", + "CDH2_MUT 0 0 0 \n", + "LOC100130776_MUT 0 0 0 \n", + "HDAC6_MUT 0 0 0 \n", + "\n", + " SF268_CENTRAL_NERVOUS_SYSTEM SF539_CENTRAL_NERVOUS_SYSTEM \\\n", + "Name \n", + "AKT3_MUT 0 0 \n", + "ABI1_MUT 0 0 \n", + "CDH2_MUT 0 0 \n", + "LOC100130776_MUT 0 0 \n", + "HDAC6_MUT 0 0 \n", + "\n", + " SNB75_CENTRAL_NERVOUS_SYSTEM HOP92_LUNG \n", + "Name \n", + "AKT3_MUT 0 0 \n", + "ABI1_MUT 0 0 \n", + "CDH2_MUT 0 0 \n", + "LOC100130776_MUT 0 0 \n", + "HDAC6_MUT 0 0 \n", + "\n", + "[5 rows x 1031 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load CCLE Mutation Data\n", + "#ccle_mut_file_name = os.path.join('..', '..', 'onco-gps-paper-analysis', 'data', \n", + "# 'mutation__gene_x_ccle_cellline.gct')\n", + "ccle_mut_file_name = os.path.join('..','data','CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct')\n", + "ccle_all_mut_df = pd.read_table(ccle_mut_file_name, skiprows=2, index_col=0)\n", + "print(ccle_all_mut_df.shape)\n", + "ccle_all_mut_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nagampv/anaconda3/envs/pancancer/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (3,25,26,27,30,31) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1175706, 31)\n" + ] + }, + { + "data": { + "text/html": [ + "
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Hugo_SymbolEntrez_Gene_IdNCBI_BuildChromosomeStart_positionEnd_positionStrandVariant_ClassificationVariant_TypeReference_Allele...isCOSMIChotspotCOSMIChsCntExAC_AFWES_ACSangerWES_ACSangerRecalibWES_ACRNAseq_ACHC_ACRD_ACWGS_AC
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2 rows × 31 columns

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" + ], + "text/plain": [ + " Hugo_Symbol Entrez_Gene_Id NCBI_Build Chromosome \\\n", + "Tumor_Sample_Barcode \n", + "22RV1_PROSTATE AGRN 375790 37 1 \n", + "22RV1_PROSTATE ATAD3A 55210 37 1 \n", + "\n", + " Start_position End_position Strand \\\n", + "Tumor_Sample_Barcode \n", + "22RV1_PROSTATE 979072 979072 + \n", + "22RV1_PROSTATE 1459233 1459233 + \n", + "\n", + " Variant_Classification Variant_Type Reference_Allele \\\n", + "Tumor_Sample_Barcode \n", + "22RV1_PROSTATE Silent SNP A \n", + "22RV1_PROSTATE Silent SNP A \n", + "\n", + " ... isCOSMIChotspot COSMIChsCnt ExAC_AF WES_AC \\\n", + "Tumor_Sample_Barcode ... \n", + "22RV1_PROSTATE ... False 0 NaN 27:24 \n", + "22RV1_PROSTATE ... False 0 0.000008 29:49 \n", + "\n", + " SangerWES_AC SangerRecalibWES_AC RNAseq_AC HC_AC RD_AC \\\n", + "Tumor_Sample_Barcode \n", + "22RV1_PROSTATE 9:10 9:12 104:20 NaN NaN \n", + "22RV1_PROSTATE 33:40 30:38 315:308 NaN NaN \n", + "\n", + " WGS_AC \n", + "Tumor_Sample_Barcode \n", + "22RV1_PROSTATE 15:13 \n", + "22RV1_PROSTATE 17:31 \n", + "\n", + "[2 rows x 31 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load CCLE Variant Data\n", + "#ccle_maf_file = 'https://data.broadinstitute.org/ccle/CCLE_DepMap_18Q1_maf_20180207.txt'\n", + "ccle_maf_file = os.path.join('..','data','CCLE_DepMap_18Q1_maf_20180207.txt')\n", + "ccle_maf_df = pd.read_table(ccle_maf_file, index_col=15)\n", + "print(ccle_maf_df.shape)\n", + "ccle_maf_df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1030, 6)\n" + ] + }, + { + "data": { + "text/html": [ + "
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NameAKT1_MUTPIK3CA_MUTKRAS_MUTERBB2_MUTBRAF_MUTpi3k_status
DMS53_LUNG000000
SW1116_LARGE_INTESTINE001001
NCIH1694_LUNG000000
P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE000000
HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE000000
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" + ], + "text/plain": [ + "Name AKT1_MUT PIK3CA_MUT KRAS_MUT \\\n", + "DMS53_LUNG 0 0 0 \n", + "SW1116_LARGE_INTESTINE 0 0 1 \n", + "NCIH1694_LUNG 0 0 0 \n", + "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 0 0 0 \n", + "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 0 0 0 \n", + "\n", + "Name ERBB2_MUT BRAF_MUT pi3k_status \n", + "DMS53_LUNG 0 0 0 \n", + "SW1116_LARGE_INTESTINE 0 0 1 \n", + "NCIH1694_LUNG 0 0 0 \n", + "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 0 0 0 \n", + "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 0 0 0 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Identify all cell lines with mutations in pi3k_gain genes, also subset pten mutant samples\n", + "pi3k_genes = ['AKT1_MUT', 'PIK3CA_MUT', 'KRAS_MUT', 'ERBB2_MUT']\n", + "pi3k_status = ccle_all_mut_df.loc[pi3k_genes, :].T.apply(max, axis=1)\n", + "\n", + "# BRAF mutations do not contribute to Ras status in this case\n", + "ccle_mut_df = (\n", + " ccle_all_mut_df.loc[pi3k_genes + ['BRAF_MUT'], :].T\n", + " .assign(pi3k_status=pi3k_status).drop(['Description'])\n", + " )\n", + "print(ccle_mut_df.shape)\n", + "ccle_mut_df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(913, 8)\n" + ] + }, + { + "data": { + "text/html": [ + "
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AKT1_MUTPIK3CA_MUTKRAS_MUTERBB2_MUTBRAF_MUTpi3k_statusweightsample_name
cell_line
UACC893_BREAST0100010.989817UACC893_BREAST
CALU3_LUNG0000000.987227CALU3_LUNG
OE19_OESOPHAGUS0000000.978839OE19_OESOPHAGUS
ZR7530_BREAST0000000.978471ZR7530_BREAST
BT474_BREAST0100010.969544BT474_BREAST
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" + ], + "text/plain": [ + " AKT1_MUT PIK3CA_MUT KRAS_MUT ERBB2_MUT BRAF_MUT pi3k_status \\\n", + "cell_line \n", + "UACC893_BREAST 0 1 0 0 0 1 \n", + "CALU3_LUNG 0 0 0 0 0 0 \n", + "OE19_OESOPHAGUS 0 0 0 0 0 0 \n", + "ZR7530_BREAST 0 0 0 0 0 0 \n", + "BT474_BREAST 0 1 0 0 0 1 \n", + "\n", + " weight sample_name \n", + "cell_line \n", + "UACC893_BREAST 0.989817 UACC893_BREAST \n", + "CALU3_LUNG 0.987227 CALU3_LUNG \n", + "OE19_OESOPHAGUS 0.978839 OE19_OESOPHAGUS \n", + "ZR7530_BREAST 0.978471 ZR7530_BREAST \n", + "BT474_BREAST 0.969544 BT474_BREAST " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Join classifier scores with mutation status\n", + "ccle_full_df = ccle_mut_df.join(result_ccle.T).dropna()\n", + "ccle_full_df = ccle_full_df.assign(sample_name = ccle_full_df.index)\n", + "ccle_full_df = ccle_full_df.sort_values(by='weight', ascending=False)\n", + "ccle_full_df.index.name = 'cell_line'\n", + "print(ccle_full_df.shape)\n", + "ccle_full_df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Write CCLE Scores to file\n", + "ccle_scores_file = os.path.join('..', 'results', 'ccle_pi3k_gain_BRAF_classifier_scores.tsv')\n", + "ccle_full_df.to_csv(ccle_scores_file, sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Use Seaborn for the 2nd plot\n", + "sns.set(style=\"whitegrid\")\n", + "sns.set_context(\"paper\", rc={\"font.size\":11, \"axes.titlesize\":11, \"axes.labelsize\":16,\n", + " 'xtick.labelsize':11, 'ytick.labelsize':11})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform a t-test on classifier weights across groups" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pi3k Status:\n", + "Ttest_indResult(statistic=7.591881252558332, pvalue=2.2345338238603483e-13)\n", + "\n", + "BRAF Status in PI3K Wild-Type Samples:\n", + "Ttest_indResult(statistic=-2.989722005505215, pvalue=0.003575401233788251)\n", + "\n", + "BRAF Status in PI3K mutant-Type Samples:\n", + "Ttest_indResult(statistic=3.3057259301818624, pvalue=0.0024243017201062696)\n" + ] + } + ], + "source": [ + "# pi3k mutant vs. pi3k wildtype\n", + "pi3k_mutant = ccle_full_df[ccle_full_df['pi3k_status'] == 1]\n", + "pi3k_wt = ccle_full_df[ccle_full_df['pi3k_status'] == 0]\n", + "\n", + "# Also interested in BRAF status within Ras wildtype samples\n", + "BRAF_mutant = pi3k_wt[pi3k_wt['BRAF_MUT'] == 1]\n", + "BRAF_wt = pi3k_wt[pi3k_wt['BRAF_MUT'] == 0]\n", + "\n", + "# Also interested in PTEN status within PI3k gain_mutant samples\n", + "BRAF_mutant_2 = pi3k_mutant[pi3k_mutant['BRAF_MUT'] == 1]\n", + "BRAF_wt_2 = pi3k_mutant[pi3k_mutant['BRAF_MUT'] == 0]\n", + "\n", + "# Output t-test results\n", + "t_results_pi3k = ttest_ind(a = pi3k_mutant['weight'],\n", + " b = pi3k_wt['weight'], equal_var = False)\n", + "print('pi3k Status:')\n", + "print(t_results_pi3k)\n", + "\n", + "t_results_BRAF = ttest_ind(a = BRAF_mutant['weight'],\n", + " b = BRAF_wt['weight'], equal_var = False)\n", + "print('\\nBRAF Status in PI3K Wild-Type Samples:')\n", + "print(t_results_BRAF)\n", + "\n", + "t_results_BRAF_2 = ttest_ind(a = BRAF_mutant_2['weight'],\n", + " b = BRAF_wt_2['weight'], equal_var = False)\n", + "print('\\nBRAF Status in PI3K mutant-Type Samples:')\n", + "print(t_results_BRAF_2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Results\n", + "x1, x2 = 0, 1\n", + "x3, x4 = -0.2, 0.2\n", + "x5, x6 = 0.8, 1.2\n", + "y1, y2,y3, h = 1.17, 1.0,1.02, 0.03\n", + "\n", + "\n", + "plt.rcParams['figure.figsize']=(3.5, 4)\n", + "ax = sns.boxplot(x=\"pi3k_status\", y=\"weight\", data=ccle_full_df,\n", + " hue='BRAF_MUT', palette = {0: \"whitesmoke\", 1: 'gainsboro'},\n", + " fliersize=0)\n", + "ax = sns.stripplot(x='pi3k_status', y='weight', hue='BRAF_MUT',\n", + " data=ccle_full_df, \n", + " dodge=True, edgecolor='gray',\n", + " palette = {1: \"seagreen\", 0: 'goldenrod'},\n", + " jitter=0.25, size=2, alpha=0.65)\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "l = plt.legend(handles[2:4], ['Wild-Type', 'Mutant'], bbox_to_anchor=(.63, 0.2), loc=2, borderaxespad=0.)\n", + "l.set_title(\"BRAF\")\n", + "ax.axes.set_ylim(0, 1.3)\n", + "ax.set_yticklabels([0, 0.2, 0.4, 0.6, 0.8, 1, ''])\n", + "ax.set_xticklabels(['Pi3k Wild-Type', 'Pi3k Mutant'])\n", + "ax.set_ylabel('Pi3k Classifier Score')\n", + "ax.set_xlabel('CCLE Data')\n", + "ax.legend\n", + "plt.axhline(0.5, color='black', linestyle='dashed', linewidth=1)\n", + "\n", + "\n", + "# Add PI3K T-Test Results\n", + "plt.plot([x1, x1, x2, x2], [y1, y1+h, y1+h, y1], lw=1.2, c='black')\n", + "plt.text(.5, y1+h, \"{:.2E}\".format(Decimal(t_results_pi3k.pvalue)),\n", + " ha='center', va='bottom', color=\"black\")\n", + "\n", + "# Add BRAF t-test results\n", + "plt.plot([x3, x3, x4, x4], [y2, y2+h, y2+h, y2], lw=1.2, c='black')\n", + "plt.text(0, y2+h, \"{:.2E}\".format(Decimal(t_results_BRAF.pvalue)),\n", + " ha='center', va='bottom', color=\"black\")\n", + "\n", + "# Add BRAF t-test results\n", + "plt.plot([x5, x5, x6, x6], [y3, y3+h, y3+h, y3], lw=1.2, c='black')\n", + "plt.text(1, y3+h, \"{:.2E}\".format(Decimal(t_results_BRAF_2.pvalue)),\n", + " ha='center', va='bottom', color=\"black\")\n", + "\n", + "plt.tight_layout()\n", + "ccle_fig_file = os.path.join('..', 'figures', 'cell_line', 'ccle_pi3k_wild_mut_BRAF_predictions.pdf')\n", + "plt.savefig(ccle_fig_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What percentage of correct classifications in CCLE data?" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Assign a label to what the predictions are given classifier scores\n", + "ccle_full_df = ccle_full_df.assign(predictions = 'wild-type')\n", + "ccle_full_df.loc[ccle_full_df['weight'] > 0.5, 'predictions'] = 'mutant'" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Stratify cell lines based on predictions and ground truth status\n", + "positive_pi3k_predictions_ccle = ccle_full_df[ccle_full_df['weight'] > 0.5]\n", + "negative_pi3k_predictions_ccle = ccle_full_df[ccle_full_df['weight'] <= 0.5]\n", + "\n", + "positive_pi3k_lines_ccle = ccle_full_df[ccle_full_df['pi3k_status'] == 1]\n", + "negative_pi3k_lines_ccle = ccle_full_df[ccle_full_df['pi3k_status'] == 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "wild-type 398\n", + "mutant 270\n", + "Name: predictions, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Of wild-type Ras cell lines, how many are predicted correctly?\n", + "# True Negative Rate, Specificity\n", + "negative_pi3k_lines_ccle['predictions'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mutant 170\n", + "wild-type 75\n", + "Name: predictions, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Of mutated Ras cell lines, how many are predicted correctly?\n", + "# True Positive Rate (TPR), Recall, Sensitivity\n", + "positive_pi3k_lines_ccle['predictions'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "398 out of 473 Pi3k wild-type predictions are true (84.1%)\n" + ] + } + ], + "source": [ + "# Of the wild-type predictions, how many are actually wild-type?\n", + "# Negative Predictive Value (NPV)\n", + "neg_ccle_results = negative_pi3k_predictions_ccle['pi3k_status'].value_counts()\n", + "true_neg = neg_ccle_results[0]\n", + "predicted_condition_neg = neg_ccle_results.sum()\n", + "\n", + "print('{} out of {} Pi3k wild-type predictions '\n", + " 'are true ({:.1f}%)'.format(true_neg, predicted_condition_neg,\n", + " true_neg * 100 / predicted_condition_neg))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "170 out of 440 Pi3k mutation predictions are true (38.6%)\n" + ] + } + ], + "source": [ + "# Of the mutated predictions, how many are actually mutated?\n", + "# Positive Predictive Value (PPV) -or- precision\n", + "pos_ccle_results = positive_pi3k_predictions_ccle['pi3k_status'].value_counts()\n", + "false_pos, true_pos = pos_ccle_results\n", + "predicted_condition_pos = pos_ccle_results.sum()\n", + "\n", + "print('{} out of {} Pi3k mutation predictions '\n", + " 'are true ({:.1f}%)'.format(true_pos, predicted_condition_pos,\n", + " true_pos * 100 / predicted_condition_pos))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "568 of 913 Total cell lines predicted correctly (62.2%)\n" + ] + } + ], + "source": [ + "total_correct = true_pos + true_neg\n", + "print('{} of {} Total cell lines '\n", + " 'predicted correctly ({:.1f}%)'.format(total_correct, ccle_full_df.shape[0],\n", + " total_correct * 100 / ccle_full_df.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17 of 270 total false positives have BRAF mutations (6.3%)\n" + ] + } + ], + "source": [ + "# Of the False positives, how many are BRAF mutant?\n", + "wt_pi3k_braf_ccle = positive_pi3k_predictions_ccle[positive_pi3k_predictions_ccle['pi3k_status'] == 0]\n", + "braf_neg, braf_pos = wt_pi3k_braf_ccle['BRAF_MUT'].value_counts()\n", + "\n", + "print('{} of {} total false positives '\n", + " 'have BRAF mutations ({:.1f}%)'.format(braf_pos, false_pos,\n", + " braf_pos * 100 / false_pos))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Including BRAF mutants, 187 of 440 PI3K mutation predictions have pi3k pathway mutations (42.5%)\n" + ] + } + ], + "source": [ + "# If include BRAF mutations, how many correct\n", + "correct_braf = wt_pi3k_braf_ccle['BRAF_MUT'].value_counts()[1]\n", + "true_pos_with_braf = true_pos + correct_braf\n", + "print('Including BRAF mutants, {} of {} PI3K mutation predictions '\n", + " 'have pi3k pathway mutations ({:.1f}%)'.format(true_pos_with_braf,\n", + " predicted_condition_pos,\n", + " true_pos_with_braf * 100 / predicted_condition_pos))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of the false positives, there are 17 BRAF mutated cell lines and 253 BRAF wild-type cell lines\n" + ] + } + ], + "source": [ + "print('Of the false positives, there are {} BRAF mutated cell lines '\n", + " 'and {} BRAF wild-type cell lines'.format(braf_pos, braf_neg))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In all of CCLE, there are 87 BRAF mutated cell lines and 826 BRAF wild-type cell lines\n" + ] + } + ], + "source": [ + "total_braf_wildtype, total_braf_mut = ccle_full_df['BRAF_MUT'].value_counts()\n", + "print('In all of CCLE, there are {} BRAF mutated cell lines '\n", + " 'and {} BRAF wild-type cell lines'.format(total_braf_mut, total_braf_wildtype))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What Ras mutations are identified in False Negatives?\n", + "\n", + "**Variant level predictions in the CCLE**" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "913" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# How many MAF tumors also have CCLE and mutation data?\n", + "len(set(ccle_full_df.index).intersection(set(ccle_maf_df.index)))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Of the false negaves, what RAS mutations do they harbor?\n", + "false_negatives_df = negative_pi3k_predictions_ccle.query('pi3k_status == 1')\n", + "common_neg_ccle_samples = set(ccle_maf_df.index).intersection(set(false_negatives_df.index))\n", + "false_neg_maf_df = ccle_maf_df.loc[list(common_neg_ccle_samples), :]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# What about true positives?\n", + "true_positives_df = positive_pi3k_predictions_ccle[positive_pi3k_predictions_ccle['pi3k_status'] == 1]\n", + "common_pos_ccle_samples = set(ccle_maf_df.index).intersection(set(true_positives_df.index))\n", + "true_pos_maf = ccle_maf_df.loc[list(common_pos_ccle_samples), :]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Subset to only Ras genes 'AKT1_MUT', 'PIK3CA_MUT', 'KRAS_MUT', 'ERBB2_MUT'\n", + "false_neg_ras = false_neg_maf_df.query('Hugo_Symbol in [\"KRAS\", \"AKT1\", \"ERBB2\", \"PIK3CA\"]')\n", + "tru_pos_ras = true_pos_maf.query('Hugo_Symbol in [\"KRAS\", \"AKT1\", \"ERBB2\", \"PIK3CA\"]')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove duplicate cell-lines. Assume 1 cosmic variant supercedes lack of cosmic\n", + "false_neg_dup = false_neg_ras.groupby('Tumor_Sample_Barcode')['isCOSMIChotspot']\n", + "\n", + "cosmic_false_neg = (\n", + " false_neg_dup.value_counts()\n", + " .reset_index(name='count')\n", + " .sort_values(by='isCOSMIChotspot')\n", + " .drop_duplicates(subset='Tumor_Sample_Barcode', keep='last')\n", + ")\n", + "\n", + "n_false_neg_cosmic = cosmic_false_neg['isCOSMIChotspot'].value_counts()[1]\n", + "n_false_neg_obs = cosmic_false_neg['isCOSMIChotspot'].shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True 59\n", + "False 15\n", + "Name: isCOSMIChotspot, dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "True 0.797297\n", + "False 0.202703\n", + "Name: isCOSMIChotspot, dtype: float64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What is the proportion of COSMIC variant (True) to non-COSMIC variant (False)\n", + "print(cosmic_false_neg['isCOSMIChotspot'].value_counts())\n", + "cosmic_false_neg['isCOSMIChotspot'].value_counts(normalize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove duplicate cell-lines. Assume 1 cosmic variant supercedes lack of cosmic\n", + "tru_pos_dup = tru_pos_ras.groupby('Tumor_Sample_Barcode')['isCOSMIChotspot']\n", + "\n", + "cosmic_true_pos = (\n", + " tru_pos_dup.value_counts()\n", + " .reset_index(name='count')\n", + " .sort_values(by='isCOSMIChotspot')\n", + " .drop_duplicates(subset='Tumor_Sample_Barcode', keep='last')\n", + ")\n", + "\n", + "n_true_pos_cosmic = cosmic_true_pos['isCOSMIChotspot'].value_counts()[1]\n", + "n_tru_pos_obs = cosmic_true_pos['isCOSMIChotspot'].shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True 147\n", + "False 23\n", + "Name: isCOSMIChotspot, dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "True 0.864706\n", + "False 0.135294\n", + "Name: isCOSMIChotspot, dtype: float64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(cosmic_true_pos['isCOSMIChotspot'].value_counts())\n", + "cosmic_true_pos['isCOSMIChotspot'].value_counts(normalize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chi Square = 1.7817635604283832, p value = 0.1819329204145474\n", + "There is a no significant difference in the expected proportion of COSMIC variants.\n" + ] + } + ], + "source": [ + "# Test if the proportions of observed COSMIC variants are significantly different\n", + "# between true positives and false negatives. All of these samples have Ras mutations.\n", + "# The question is asking if the proportion of Ras variants annotated in the COSMIC\n", + "# database is lower in False negative tumors (those the classifier predicted as\n", + "# Ras wild-type) than in True positive tumors\n", + "cosmic_prop_chi = proportions_chisquare(count = [n_false_neg_cosmic, n_true_pos_cosmic],\n", + " nobs = [n_false_neg_obs, n_tru_pos_obs])\n", + "\n", + "print('Chi Square = {}, p value = {}'.format(cosmic_prop_chi[0], cosmic_prop_chi[1]))\n", + "print('There is a no significant difference in the expected proportion of COSMIC variants.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add CCLE Variant Scores (nucleotide and amino acid) to Supplementary Data Files" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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genesog_tsg
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Hugo_SymbolProtein_ChangecDNA_ChangeAKT1_MUTPIK3CA_MUTKRAS_MUTERBB2_MUTBRAF_MUTpi3k_statusweightsample_namepredictions
A204_SOFT_TISSUEPIK3CBp.Y176Cc.527A>G0000000.430099A204_SOFT_TISSUEwild-type
A204_SOFT_TISSUETSC2p.L764Lc.2292G>A0000000.430099A204_SOFT_TISSUEwild-type
A204_SOFT_TISSUENF1p.L1274Fc.3820C>T0000000.430099A204_SOFT_TISSUEwild-type
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" + ], + "text/plain": [ + " Hugo_Symbol Protein_Change cDNA_Change AKT1_MUT PIK3CA_MUT \\\n", + "A204_SOFT_TISSUE PIK3CB p.Y176C c.527A>G 0 0 \n", + "A204_SOFT_TISSUE TSC2 p.L764L c.2292G>A 0 0 \n", + "A204_SOFT_TISSUE NF1 p.L1274F c.3820C>T 0 0 \n", + "\n", + " KRAS_MUT ERBB2_MUT BRAF_MUT pi3k_status weight \\\n", + "A204_SOFT_TISSUE 0 0 0 0 0.430099 \n", + "A204_SOFT_TISSUE 0 0 0 0 0.430099 \n", + "A204_SOFT_TISSUE 0 0 0 0 0.430099 \n", + "\n", + " sample_name predictions \n", + "A204_SOFT_TISSUE A204_SOFT_TISSUE wild-type \n", + "A204_SOFT_TISSUE A204_SOFT_TISSUE wild-type \n", + "A204_SOFT_TISSUE A204_SOFT_TISSUE wild-type " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Subset MAF file to Ras pathway variants and merge with CCLE classifier scores\n", + "pi3k_pathway_genes = pi3k_core_df['genes'].tolist()\n", + "all_common_lines = set(ccle_maf_df.index).intersection(set(ccle_full_df.index))\n", + "\n", + "# Subset to common cell lines\n", + "subset_maf = ccle_maf_df.loc[list(all_common_lines), :]\n", + "subset_maf = (\n", + " subset_maf.query('Hugo_Symbol in @pi3k_pathway_genes')\n", + " .loc[:, ['Hugo_Symbol', 'Protein_Change', 'cDNA_Change']]\n", + " .merge(ccle_full_df, left_index=True, right_index=True)\n", + ")\n", + "\n", + "subset_maf.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the mean classifier scores for CCLE nucleotide variants\n", + "mean_nuc_data = (\n", + " pd.DataFrame(subset_maf\n", + " .groupby(['cDNA_Change', 'Hugo_Symbol'])['weight']\n", + " .mean())\n", + ")\n", + "mean_nuc_data.columns = ['ccle_mean_weight']\n", + "mean_nuc_data = mean_nuc_data.reset_index()\n", + "\n", + "# Get the sd classifier scores for CCLE variants\n", + "sd_nuc_data = (\n", + " pd.DataFrame(subset_maf\n", + " .groupby(['cDNA_Change', 'Hugo_Symbol'])['weight']\n", + " .std())\n", + ")\n", + "sd_nuc_data.columns = ['ccle_sd_weight']\n", + "sd_nuc_data = sd_nuc_data.reset_index()\n", + "\n", + "# Counts of CCLE variants altering amino acids\n", + "count_nuc_data = (\n", + " pd.DataFrame(subset_maf\n", + " .groupby(['cDNA_Change', 'Hugo_Symbol'])['weight']\n", + " .count())\n", + ")\n", + "count_nuc_data.columns = ['ccle_count']\n", + "count_nuc_data = count_nuc_data.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cDNA_ChangeHugo_Symbolccle_mean_weightccle_sd_weightccle_count
858c.3140A>GPIK3CA0.6486780.17824720
411c.181C>ANRAS0.5103730.16102221
942c.34G>TKRAS0.5418690.13300023
962c.35G>TKRAS0.6145440.15887333
958c.35G>AKRAS0.6265360.13069147
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" + ], + "text/plain": [ + " cDNA_Change Hugo_Symbol ccle_mean_weight ccle_sd_weight ccle_count\n", + "858 c.3140A>G PIK3CA 0.648678 0.178247 20\n", + "411 c.181C>A NRAS 0.510373 0.161022 21\n", + "942 c.34G>T KRAS 0.541869 0.133000 23\n", + "962 c.35G>T KRAS 0.614544 0.158873 33\n", + "958 c.35G>A KRAS 0.626536 0.130691 47" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Merge protein data\n", + "nuc_merge_on = ['Hugo_Symbol', 'cDNA_Change']\n", + "nuc_change_df = (\n", + " mean_nuc_data.merge(sd_nuc_data,\n", + " left_on=nuc_merge_on, right_on=nuc_merge_on)\n", + " .merge(count_nuc_data, left_on=nuc_merge_on, right_on=nuc_merge_on)\n", + ")\n", + "\n", + "nuc_change_df.sort_values('ccle_count').tail(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "data_s4_file = os.path.join('..', 'classifiers', 'ERBB2_PIK3CA_KRAS_AKT1', 'tables',\n", + " 'nucleotide_mutation_scores.tsv')\n", + "data_s4_df = pd.read_table(data_s4_file)\n", + "# Merge the CCLE nucleotide scores\n", + "data_s4_df = data_s4_df.merge(nuc_change_df, left_on = ['Hugo_Symbol', 'HGVSc'],\n", + " right_on = ['Hugo_Symbol', 'cDNA_Change'],\n", + " how='outer')\n", + "updated_data_s4_df = data_s4_df.sort_values(by='count', ascending=False)\n", + "updated_data_s4_df.head()\n", + "updated_data_s4_file = os.path.join('..', 'classifiers', 'ERBB2_PIK3CA_KRAS_AKT1', 'tables', 'updated_Data_S4.csv')\n", + "updated_data_s4_df.to_csv(updated_data_s4_file, sep=',', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the mean classifier scores for CCLE variants\n", + "mean_protein_data = (\n", + " pd.DataFrame(subset_maf\n", + " .groupby(['Protein_Change', 'Hugo_Symbol'])['weight']\n", + " .mean())\n", + ")\n", + "mean_protein_data.columns = ['ccle_mean_weight']\n", + "mean_protein_data = mean_protein_data.reset_index()\n", + "\n", + "# Get the sd classifier scores for CCLE variants\n", + "sd_protein_data = (\n", + " pd.DataFrame(subset_maf\n", + " .groupby(['Protein_Change', 'Hugo_Symbol'])['weight']\n", + " .std())\n", + ")\n", + "sd_protein_data.columns = ['ccle_sd_weight']\n", + "sd_protein_data = sd_protein_data.reset_index()\n", + "\n", + "# Counts of CCLE variants altering amino acids\n", + "count_protein_data = (\n", + " pd.DataFrame(subset_maf\n", + " .groupby(['Protein_Change', 'Hugo_Symbol'])['weight']\n", + " .count())\n", + ")\n", + "count_protein_data.columns = ['ccle_count']\n", + "count_protein_data = count_protein_data.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Protein_ChangeHugo_Symbolccle_mean_weightccle_sd_weightccle_count
358p.E545KPIK3CA0.5543420.19464720
1159p.Q61KNRAS0.5103730.16102221
446p.G12CKRAS0.5418690.13300023
455p.G12VKRAS0.6145440.15887333
449p.G12DKRAS0.6265360.13069147
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" + ], + "text/plain": [ + " Protein_Change Hugo_Symbol ccle_mean_weight ccle_sd_weight ccle_count\n", + "358 p.E545K PIK3CA 0.554342 0.194647 20\n", + "1159 p.Q61K NRAS 0.510373 0.161022 21\n", + "446 p.G12C KRAS 0.541869 0.133000 23\n", + "455 p.G12V KRAS 0.614544 0.158873 33\n", + "449 p.G12D KRAS 0.626536 0.130691 47" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Merge protein data\n", + "merge_on = ['Hugo_Symbol', 'Protein_Change']\n", + "protein_change_df = (\n", + " mean_protein_data.merge(sd_protein_data,\n", + " left_on=merge_on, right_on=merge_on)\n", + " .merge(count_protein_data, left_on=merge_on, right_on=merge_on)\n", + ")\n", + "\n", + "protein_change_df.sort_values('ccle_count').tail(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert amino acid to 3 letters\n", + "protein_convert = [''.join([aa[x] if x in aa.keys() else x for x in y]) \n", + " for y in protein_change_df['Protein_Change']]\n", + "\n", + "protein_change_df = protein_change_df.assign(conversion = protein_convert)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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HGVSpVariant_ClassificationHugo_SymbolMeanSDcountlow_CIhigh_CIccle_mean_weightccle_sd_weightccle_countconversion
0p.Glu545LysMissense_MutationPIK3CA0.7099140.134668273.00.6942240.7269300.5543420.19464720.0p.Glu545Lys
1p.His1047ArgMissense_MutationPIK3CA0.7229020.150649231.00.7031520.7413690.6486780.17824720.0p.His1047Arg
2p.Glu542LysMissense_MutationPIK3CA0.6789220.138295177.00.6587050.6988110.5755510.19828611.0p.Glu542Lys
3p.Gly12AspMissense_MutationKRAS0.6855150.126081171.00.6640200.7030340.6265360.13069147.0p.Gly12Asp
4p.Gly12ValMissense_MutationKRAS0.6929150.119621164.00.6750940.7114100.6145440.15887333.0p.Gly12Val
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" + ], + "text/plain": [ + " HGVSp Variant_Classification Hugo_Symbol Mean SD count \\\n", + "0 p.Glu545Lys Missense_Mutation PIK3CA 0.709914 0.134668 273.0 \n", + "1 p.His1047Arg Missense_Mutation PIK3CA 0.722902 0.150649 231.0 \n", + "2 p.Glu542Lys Missense_Mutation PIK3CA 0.678922 0.138295 177.0 \n", + "3 p.Gly12Asp Missense_Mutation KRAS 0.685515 0.126081 171.0 \n", + "4 p.Gly12Val Missense_Mutation KRAS 0.692915 0.119621 164.0 \n", + "\n", + " low_CI high_CI ccle_mean_weight ccle_sd_weight ccle_count \\\n", + "0 0.694224 0.726930 0.554342 0.194647 20.0 \n", + "1 0.703152 0.741369 0.648678 0.178247 20.0 \n", + "2 0.658705 0.698811 0.575551 0.198286 11.0 \n", + "3 0.664020 0.703034 0.626536 0.130691 47.0 \n", + "4 0.675094 0.711410 0.614544 0.158873 33.0 \n", + "\n", + " conversion \n", + "0 p.Glu545Lys \n", + "1 p.His1047Arg \n", + "2 p.Glu542Lys \n", + "3 p.Gly12Asp \n", + "4 p.Gly12Val " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_s5_file = os.path.join('..', 'classifiers', 'ERBB2_PIK3CA_KRAS_AKT1', 'tables',\n", + " 'amino_acid_mutation_scores.tsv')\n", + "data_s5_df = pd.read_table(data_s5_file)\n", + "\n", + "# Merge the CCLE protein scores\n", + "data_s5_df = data_s5_df.merge(protein_change_df, left_on = ['Hugo_Symbol', 'HGVSp'],\n", + " right_on = ['Hugo_Symbol', 'conversion'],\n", + " how='outer')\n", + "\n", + "# Sort by the total number of mutations observed\n", + "updated_data_s5_df = (\n", + " data_s5_df.drop(['Protein_Change'], axis=1).sort_values(by='count', ascending=False)\n", + ")\n", + "\n", + "updated_data_s5_file = os.path.join('..', 'classifiers', 'ERBB2_PIK3CA_KRAS_AKT1','tables', 'updated_Data_S5.csv')\n", + "updated_data_s5_df.to_csv(updated_data_s5_file, sep=',', index=False)\n", + "updated_data_s5_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CCLE - Pharmacologic Efficacy\n", + "\n", + "Here, we process drug efficacy data on the CCLE dataset. Data obtained from https://portals.broadinstitute.org/ccle/data (Pharmacologic profiling) (signin required).\n", + "\n", + "A processed `.tsv` file is output to be visualized in `scripts/viz/ras_ccle_pharmacology.R`." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Primary Cell Line NameCompoundTargetDoses (uM)Activity Data (median)Activity SDNum DataFitTypeEC50 (uM)IC50 (uM)...tissueAKT1_MUTPIK3CA_MUTKRAS_MUTERBB2_MUTBRAF_MUTpi3k_statusweightsample_namepredictions
A172_CENTRAL_NERVOUS_SYSTEMA172AEW541IGF1R.0025,.0080,.025,.080,.25,.80,2.53,8-.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-225.84,1.59,2.77,5.89,2.43,3.25,4.54,7.738Sigmoid8.4196218.000000...CENTRAL NERVOUS SYSTEM0000000.548974A172_CENTRAL_NERVOUS_SYSTEMmutant
A172_CENTRAL_NERVOUS_SYSTEMA172NilotinibABL.0025,.0080,.025,.080,.25,.80,2.53,8-.75,3.15,9.45,7.81,1.15,4.57,-3.7,-2.16.44,3.36,2.34,1.18,1.86,3.21,4.71,6.228ConstantNaN8.000000...CENTRAL NERVOUS SYSTEM0000000.548974A172_CENTRAL_NERVOUS_SYSTEMmutant
A172_CENTRAL_NERVOUS_SYSTEMA17217-AAGHSP90.0025,.0080,.025,.080,.25,.80,2.53,8-4.1,-3.6,-8.3,-13,-49,-67,-67,-712.33,.79,1.76,4.40,3.10,2.19,1.71,2.438Sigmoid0.1794080.267499...CENTRAL NERVOUS SYSTEM0000000.548974A172_CENTRAL_NERVOUS_SYSTEMmutant
A172_CENTRAL_NERVOUS_SYSTEMA172PHA-665752c-MET.0025,.0080,.025,.080,.25,.80,2.53,8-11,-.73,3.40,1.00,.26,5.42,7.48,10.4.86,7.72,2.80,14.1,6.64,2.25,7.98,6.828LinearNaN8.000000...CENTRAL NERVOUS SYSTEM0000000.548974A172_CENTRAL_NERVOUS_SYSTEMmutant
A172_CENTRAL_NERVOUS_SYSTEMA172LapatinibEGFR.0025,.0080,.025,.080,.25,.80,2.53,83.48,1.18,.20,2.58,-4.6,-2.1,-8.0,-4.2.91,.27,3.61,4.82,4.66,6.00,3.26,8.818ConstantNaN8.000000...CENTRAL NERVOUS SYSTEM0000000.548974A172_CENTRAL_NERVOUS_SYSTEMmutant
\n", + "

5 rows × 22 columns

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" + ], + "text/plain": [ + " Primary Cell Line Name Compound Target \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM A172 AEW541 IGF1R \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172 Nilotinib ABL \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172 17-AAG HSP90 \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172 PHA-665752 c-MET \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172 Lapatinib EGFR \n", + "\n", + " Doses (uM) \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM .0025,.0080,.025,.080,.25,.80,2.53,8 \n", + "A172_CENTRAL_NERVOUS_SYSTEM .0025,.0080,.025,.080,.25,.80,2.53,8 \n", + "A172_CENTRAL_NERVOUS_SYSTEM .0025,.0080,.025,.080,.25,.80,2.53,8 \n", + "A172_CENTRAL_NERVOUS_SYSTEM .0025,.0080,.025,.080,.25,.80,2.53,8 \n", + "A172_CENTRAL_NERVOUS_SYSTEM .0025,.0080,.025,.080,.25,.80,2.53,8 \n", + "\n", + " Activity Data (median) \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM -.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-22 \n", + "A172_CENTRAL_NERVOUS_SYSTEM -.75,3.15,9.45,7.81,1.15,4.57,-3.7,-2.1 \n", + "A172_CENTRAL_NERVOUS_SYSTEM -4.1,-3.6,-8.3,-13,-49,-67,-67,-71 \n", + "A172_CENTRAL_NERVOUS_SYSTEM -11,-.73,3.40,1.00,.26,5.42,7.48,10.4 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 3.48,1.18,.20,2.58,-4.6,-2.1,-8.0,-4.2 \n", + "\n", + " Activity SD \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM 5.84,1.59,2.77,5.89,2.43,3.25,4.54,7.73 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 6.44,3.36,2.34,1.18,1.86,3.21,4.71,6.22 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 2.33,.79,1.76,4.40,3.10,2.19,1.71,2.43 \n", + "A172_CENTRAL_NERVOUS_SYSTEM .86,7.72,2.80,14.1,6.64,2.25,7.98,6.82 \n", + "A172_CENTRAL_NERVOUS_SYSTEM .91,.27,3.61,4.82,4.66,6.00,3.26,8.81 \n", + "\n", + " Num Data FitType EC50 (uM) IC50 (uM) \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM 8 Sigmoid 8.419621 8.000000 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 8 Constant NaN 8.000000 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 8 Sigmoid 0.179408 0.267499 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 8 Linear NaN 8.000000 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 8 Constant NaN 8.000000 \n", + "\n", + " ... tissue AKT1_MUT \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM ... CENTRAL NERVOUS SYSTEM 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM ... CENTRAL NERVOUS SYSTEM 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM ... CENTRAL NERVOUS SYSTEM 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM ... CENTRAL NERVOUS SYSTEM 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM ... CENTRAL NERVOUS SYSTEM 0 \n", + "\n", + " PIK3CA_MUT KRAS_MUT ERBB2_MUT BRAF_MUT \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0 0 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0 0 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0 0 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0 0 0 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0 0 0 \n", + "\n", + " pi3k_status weight \\\n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0.548974 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0.548974 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0.548974 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0.548974 \n", + "A172_CENTRAL_NERVOUS_SYSTEM 0 0.548974 \n", + "\n", + " sample_name predictions \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "A172_CENTRAL_NERVOUS_SYSTEM A172_CENTRAL_NERVOUS_SYSTEM mutant \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load in pharmacological results\n", + "pharm_file = os.path.join('..', 'data', 'CCLE_NP24.2009_Drug_data_2015.02.24.csv')\n", + "pharm_df = pd.read_csv(pharm_file, index_col=0)\n", + "pharm_df = pharm_df.assign(tissue = [' '.join(x[1:]) for x in pharm_df.index.str.split('_')])\n", + "\n", + "pharm_full_df = pharm_df.merge(ccle_full_df, left_index=True, right_index=True)\n", + "pharm_full_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(913, 9)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ccle_full_df.head()\n", + "ccle_full_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 457 cell lines in common\n" + ] + }, + { + "data": { + "text/plain": [ + "AZD0530 457\n", + "PF2341066 457\n", + "TAE684 457\n", + "PD-0325901 457\n", + "Lapatinib 457\n", + "Topotecan 457\n", + "TKI258 457\n", + "Nutlin-3 457\n", + "Paclitaxel 456\n", + "Erlotinib 456\n", + "AEW541 456\n", + "AZD6244 456\n", + "LBW242 456\n", + "Sorafenib 456\n", + "17-AAG 456\n", + "PHA-665752 456\n", + "Panobinostat 453\n", + "ZD-6474 450\n", + "PLX4720 450\n", + "L-685458 444\n", + "RAF265 416\n", + "PD-0332991 392\n", + "Nilotinib 379\n", + "Irinotecan 288\n", + "Name: Compound, dtype: int64" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "common_celllines_pharm = set(ccle_full_df.index).intersection(set(pharm_df.index))\n", + "print('There are {} cell lines in common'.format(len(common_celllines_pharm)))\n", + "\n", + "pharm_full_df['Compound'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LUNG 2016\n", + "HAEMATOPOIETIC AND LYMPHOID TISSUE 1585\n", + "SKIN 865\n", + "BREAST 653\n", + "PANCREAS 599\n", + "OVARY 587\n", + "CENTRAL NERVOUS SYSTEM 585\n", + "LARGE INTESTINE 489\n", + "ENDOMETRIUM 458\n", + "STOMACH 396\n", + "LIVER 388\n", + "OESOPHAGUS 339\n", + "URINARY TRACT 286\n", + "SOFT TISSUE 244\n", + "BONE 236\n", + "AUTONOMIC GANGLIA 223\n", + "PLEURA 165\n", + "KIDNEY 163\n", + "UPPER AERODIGESTIVE TRACT 132\n", + "THYROID 72\n", + "PROSTATE 48\n", + "BILIARY TRACT 24\n", + "SALIVARY GLAND 23\n", + "Name: tissue, dtype: int64" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pharm_full_df['tissue'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# What is the cell line tissue representation?\n", + "compound_heatmap = pd.pivot_table(pharm_full_df[['tissue', 'Compound']],\n", + " columns='tissue', index='Compound',\n", + " aggfunc=len)\n", + "\n", + "compound_heatmap = pd.DataFrame(compound_heatmap.unstack()).reset_index()\n", + "compound_heatmap.columns = ['tissue', 'Compound', 'count']\n", + "compound_heatmap = compound_heatmap.sort_values(by=['tissue', 'Compound'])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nagampv/anaconda3/envs/pancancer/lib/python3.6/site-packages/plotnine/utils.py:281: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " ndistinct = ids.apply(len_unique, axis=0).as_matrix()\n", + "/home/nagampv/anaconda3/envs/pancancer/lib/python3.6/site-packages/pandas/core/generic.py:4384: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n", + " object.__getattribute__(self, name)\n", + "/home/nagampv/anaconda3/envs/pancancer/lib/python3.6/site-packages/pandas/core/generic.py:4385: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.\n", + " return object.__setattr__(self, name, value)\n", + "/home/nagampv/anaconda3/envs/pancancer/lib/python3.6/site-packages/mizani/bounds.py:345: RuntimeWarning: invalid value encountered in less\n", + " outside = (x < range[0]) | (x > range[1])\n", + "/home/nagampv/anaconda3/envs/pancancer/lib/python3.6/site-packages/mizani/bounds.py:345: RuntimeWarning: invalid value encountered in greater\n", + " outside = (x < range[0]) | (x > range[1])\n", + "/home/nagampv/anaconda3/envs/pancancer/lib/python3.6/site-packages/matplotlib/colors.py:504: RuntimeWarning: invalid value encountered in less\n", + " xa[xa < 0] = -1\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gg.ggplot(compound_heatmap, gg.aes('factor(tissue)', 'factor(Compound)', fill='count')) + \\\n", + "gg.geom_tile(gg.aes(width=.95, height=.95)) + \\\n", + "gg.theme(axis_text_x=gg.element_text(rotation=90),\n", + " panel_background=gg.element_rect(fill='white'))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Write out pharm_full_df to file to plot in ggplot2\n", + "# plotnine does not include all the functionality required to create the plot\n", + "pharm_file = os.path.join('..', 'data', 'pharmacology_predictions_ERBB2_PIK3CA_KRAS_AKT1_ccle.tsv')\n", + "pharm_full_df.to_csv(pharm_file, sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/pi3k_count_heatmaps.py b/scripts/pi3k_count_heatmaps.py new file mode 100644 index 0000000..bed39b7 --- /dev/null +++ b/scripts/pi3k_count_heatmaps.py @@ -0,0 +1,342 @@ + +# coding: utf-8 + +# In[1]: + + +import os +import sys +import pandas as pd + +import matplotlib.pyplot as plt +import seaborn as sns + + +# In[2]: + + +# Get the current working directory +cwd = os.getcwd() + +# Ensure that the path is starting in the scripts directory +if not cwd.split('/')[-1] == 'scripts': + os.chdir(os.path.join(cwd, 'scripts')) + + +# In[3]: + +# get_ipython().run_line_magic('matplotlib', 'inline') +plt.style.use('seaborn-notebook') + + +# In[4]: + + +# Load Datasets +print("executing pi3k_count_heatmaps and loading datasets") +mut_file = os.path.join('..', 'data', 'pancan_mutation_freeze.tsv.gz') +sample_freeze_file = os.path.join('..', 'data', 'sampleset_freeze.csv') +copy_loss_file = os.path.join('..', 'data', 'copy_number_loss_status.tsv.gz') +copy_gain_file = os.path.join('..', 'data', 'copy_number_gain_status.tsv.gz') +cancer_genes_file = os.path.join('..', 'data', 'vogelstein_cancergenes.tsv') + +mutation_df = pd.read_table(mut_file, index_col=0) +sample_freeze = (pd.read_csv(sample_freeze_file, index_col=0) + .drop(['SQUAMOUS', 'COMPLETE'], axis='columns') + ) +copy_loss_df = pd.read_table(copy_loss_file, index_col=0 + ) +copy_gain_df = pd.read_table(copy_gain_file, index_col=0) +cancer_genes_df = pd.read_table(cancer_genes_file) + + +# In[5]: + + +# Load pi3k Pathway Genes +results_path= os.path.join('..', 'classifiers', 'PI3K') + +# Load pi3k Pathway Genes +genes_file = os.path.join('..', 'data', 'pi3k_genes_2.csv') +genes_df = pd.read_table(genes_file) + + +# In[6]: + + +genes_df.head() + + +# In[7]: + + +# Subset mutation data +print("mutation_df_from line 74") +print(mutation_df) +mutation_sub_df = mutation_df.loc[:, genes_df['genes']] +print("mutation_sub_df_line 74 only to pi3k pathway genes") +print(mutation_sub_df) + +# In[8]: + + +# Find if the input genes are in this master list +print("cancer_genes_df from line 49") +print(cancer_genes_df) +genes_sub = cancer_genes_df[cancer_genes_df['Gene Symbol'].isin(genes_df['genes'])] +print("genes_sub_line 86") +print(genes_sub) + +# In[9]: + + +# Add status to the Y matrix depending on if the gene is a tumor suppressor +# or an oncogene. An oncogene can be activated with copy number gains, but +# a tumor suppressor is inactivated with copy number loss +tumor_suppressor = genes_df[genes_df['og_tsg'] == 'TSG'] +oncogene = genes_df[genes_df['og_tsg'] == 'OG'] + +# Subset copy number information +copy_loss_sub_df = copy_loss_df[tumor_suppressor['genes']] +copy_gain_sub_df = copy_gain_df[oncogene['genes']] + +print("copy_loss_sub_df_line 102") +print(copy_loss_sub_df) + +print("copy_gain_sub_df_line 105") +print(copy_gain_sub_df) + +# ## Output Mutation, Copy Number, and Total Heatmap (Gene by Cancer-type) + +# In[10]: + + +mutation_sub_total_df = mutation_sub_df.assign(Total=mutation_sub_df.max(axis=1)) +mut_disease_df = mutation_sub_total_df.merge(sample_freeze, left_index=True, + right_on='SAMPLE_BARCODE') +mut_heatmap_df = mut_disease_df.groupby('DISEASE').mean() + +print("mut_heatmap_df _line 119") +print(mut_heatmap_df) +# In[11]: + +print("mut_disease_df from line 114, merge of mutations_sub_total with sample_freeze") +print(mut_disease_df) +gene_avg = mut_disease_df.mean() +gene_avg.name = 'Total' +print("gene_avg _line 127, mean of mutations from disease") +print(gene_avg) + +# In[12]: + + +mut_heatmap_df = mut_heatmap_df.append(gene_avg) +print("mut_heatmap_df_line 134 adjusted to gene_avg") +print(mut_heatmap_df) + +# In[13]: + + +sns.set_style("whitegrid") +sns.heatmap(mut_heatmap_df, linewidths=0.2, linecolor='black', + cmap='Blues_r', square=True, cbar=True) +plt.ylabel('Cancer Types', fontsize=16) +plt.xlabel('pi3k Pathway Genes', fontsize=16) +plt.savefig(os.path.join(results_path, 'mut_df.svg')) + + +# In[14]: + + +copy_df = pd.concat([copy_gain_sub_df, copy_loss_sub_df], axis=1) +copy_total_df = copy_df.assign(Total=copy_df.max(axis=1)) +copy_disease_df = copy_total_df.merge(sample_freeze, left_index=True, + right_on='SAMPLE_BARCODE') +copy_heatmap_df = copy_disease_df.groupby('DISEASE').mean() + +print("copy_heatmap_df _line 156 disease mean") +print(copy_heatmap_df) +# In[15]: + + +copy_avg = copy_disease_df.mean() +copy_avg.name = 'Total' + + +# In[16]: + + +copy_heatmap_df = copy_heatmap_df.append(copy_avg) +print("copy_heatmap_df _line 169 copy avg") +print(copy_heatmap_df) + +# In[17]: + + +sns.set_style("whitegrid") +sns.heatmap(copy_heatmap_df, linewidths=0.2, linecolor='black', + cmap='Blues_r', square=True) +plt.ylabel('Cancer Types', fontsize=16) +plt.xlabel('pi3k Pathway Genes', fontsize=16) +plt.savefig(os.path.join(results_path, 'copy_df.svg')) + + +# In[18]: + + +# Combined heatmap +print("mutation_sub_df from line 75") +print(mutation_sub_df) +print("copy_df from line 149") +print(copy_df) +comb_heat = mutation_sub_df + copy_df +comb_heat[comb_heat == 2] = 1 # Replace duplicates with just one +print("comb_heat line 193 adding mutation and copy no for pi3k genes") +print(comb_heat) + +# In[19]: + + +comb_heat_df = comb_heat.merge(sample_freeze, left_index=True, right_on='SAMPLE_BARCODE') +comb_heat_total_df = comb_heat_df.assign(Total=comb_heat_df.max(axis=1)) +comb_heatmap_df = comb_heat_total_df.groupby('DISEASE').mean() + +print("comb_heatmap_df _line 203 disease mean" ) +print(comb_heatmap_df) +# In[20]: + + +comb_avg = comb_heat_total_df.mean() +comb_avg.name = 'Total' + + +# In[21]: + + +comb_heatmap_plot = comb_heatmap_df.append(comb_avg) +print("comb_heatmap_df _line 215 appened with comb_avg" ) +print(comb_heatmap_df) + +# In[22]: + + +sns.set_style("whitegrid") +sns.heatmap(comb_heatmap_plot, linewidths=0.2, linecolor='black', + cmap='Blues_r', square=True) +plt.ylabel('Cancer Types', fontsize=16) +plt.xlabel('pi3k Pathway Genes', fontsize=16) +plt.tight_layout() +plt.savefig(os.path.join(results_path, 'combined_df.svg')) + + +# ## Generating Pathway Mapper Text Files + +# In[23]: + + +summary_score = pd.DataFrame([mut_heatmap_df.ix['Total', :], copy_heatmap_df.ix['Total', :]]) +print("summary_score _line 237 combining mut_heatmap_df and copy_heatmap_df") +print(summary_score) +# transposing the summaryscore +summary_score=summary_score.T +summary_score.columns=['mutation', 'copy_number'] +summary_score=summary_score * 100 +summary_score = summary_score.round(decimals = 1) +print("summary_score _line 244 after modification") +print(summary_score) + +# In[24]: + + +mut_heatmap_df + + +# In[25]: + +# ras pathway has 38 genes and pi3k pathway has 18 genes so this dimentions mismatches +# tum_sup_mult = pd.Series([1] * 34 + [-1] * 4 + [1]) +tum_sup_mult = pd.Series([1] * 14 + [-1] * 4 + [1]) +print("tum_sup_mult _line 258 creating a pandas Series") +print(tum_sup_mult) +tum_sup_mult.index = summary_score.index + + +# In[26]: + + +summary_score = summary_score.mul(tum_sup_mult, axis=0) +pathway_mapper_file = os.path.join(results_path, 'tables', + 'pathwaymapper_percentages.txt') +summary_score.to_csv(pathway_mapper_file, sep='\t') + + +# ## Output number of Ras events per sample + +# In[27]: + + +decision_file = os.path.join(results_path, 'classifier_decisions.tsv') +decisions_df = pd.read_table(decision_file) +decisions_df.head() +print("decisions_df_line 280") +print(decisions_df) + +# In[28]: + +other_pi3k_df = mutation_sub_df.drop(['PIK3R1', 'PIK3CA', 'AKT1'], axis=1) +other_pi3k_copy_df = copy_df.drop(['PIK3R1', 'PIK3CA', 'AKT1'], axis=1) +other_pi3k_all_df = comb_heat_df.drop(['PIK3R1', 'PIK3CA', 'AKT1'], axis=1) +print("other_pi3k_all_df _line 288") +print(other_pi3k_all_df) + +# In[29]: + + +total_pi3k_mutations = pd.DataFrame(other_pi3k_df.sum(axis=1), columns=['mutation_count']) +total_pi3k_copy_events = pd.DataFrame(other_pi3k_copy_df.sum(axis=1), columns=['copy_count']) +total_pi3k_all = pd.DataFrame(other_pi3k_all_df.sum(axis=1), columns=['all_count']) +total_pi3k_all.index = comb_heat_df['SAMPLE_BARCODE'] + + +# In[30]: + + +# Define output summary of mutation, copy, and total counts per sample by Ras pathway +count_summary = ( + decisions_df[['SAMPLE_BARCODE', 'DISEASE', 'weight']] + .merge(total_pi3k_mutations, left_on='SAMPLE_BARCODE', right_index=True) + ) +hyper_samples = decisions_df[decisions_df['hypermutated'] == 1]['SAMPLE_BARCODE'] +count_summary.ix[count_summary['SAMPLE_BARCODE'].isin(hyper_samples), + 'mutation_count'] = 'hyper' +count_summary.head() +print("count_summary _line 312") +print(count_summary) + +# In[31]: + + +count_summary['mutation_count'].value_counts() + + +# In[32]: + + +count_summary = total_pi3k_copy_events.merge(count_summary, left_index=True, + right_on='SAMPLE_BARCODE') +count_summary = total_pi3k_all.merge(count_summary, left_index=True, + right_on='SAMPLE_BARCODE') +count_summary = ( + decisions_df[['SAMPLE_BARCODE', 'total_status']] + .merge(count_summary, left_on='SAMPLE_BARCODE', right_on='SAMPLE_BARCODE') + ) +count_summary.head() +print("count_summary _line 333") +print(count_summary) + +# In[33]: + +count_summary_file = os.path.join(results_path, 'tables', + 'pi3k_events_per_sample.tsv') +count_summary.to_csv(count_summary_file, sep='\t', index=False) +print("pi3k_count_heatmaps_done") \ No newline at end of file diff --git a/scripts/visualize_decisions.py b/scripts/visualize_decisions.py index c04f01b..e233904 100644 --- a/scripts/visualize_decisions.py +++ b/scripts/visualize_decisions.py @@ -16,6 +16,8 @@ import os import argparse import pandas as pd +import matplotlib +matplotlib.use('agg') import seaborn as sns import matplotlib.pyplot as plt diff --git a/scripts/within_tissue_analysis.py b/scripts/within_tissue_analysis.py index b79eceb..2617d27 100644 --- a/scripts/within_tissue_analysis.py +++ b/scripts/within_tissue_analysis.py @@ -28,10 +28,12 @@ import pandas as pd parser = argparse.ArgumentParser() +#parser.add_argument('-g', '--genes', +# help='Comma separated string of HUGO gene symbols') parser.add_argument('-g', '--genes', - help='Comma separated string of HUGO gene symbols') + help='string of the genes to extract or genelist file') parser.add_argument('-d', '--diseases', default='Auto', - help='Comma separated string of disease types to consider') + help='comma seperated diseases list in a file') parser.add_argument('-a', '--alphas', default='0.1,0.15,0.2,0.5,0.8,1', help='the alphas for parameter sweep') parser.add_argument('-l', '--l1_ratios', default='0,0.1,0.15,0.18,0.2,0.3', @@ -41,19 +43,53 @@ parser.add_argument('-f', '--alt_folder', default='Auto', help='location to save') +parser.add_argument('-x', '--x_matrix', default=None, + help='Filename of features to use in model') +parser.add_argument( '--filename_mut', default=None, + help='Filename of sample/gene mutations to use in model') +parser.add_argument( '--filename_mut_burden', default=None, + help='Filename of sample mutation burden to use in model') +parser.add_argument( '--filename_sample', default=None, + help='Filename of patient/samples to use in model') +parser.add_argument( '--filename_copy_loss', default=None, + help='Filename of copy number loss') +parser.add_argument( '--filename_copy_gain', default=None, + help='Filename of copy number gain') +parser.add_argument( '--filename_cancer_gene_classification', default=None, + help='Filename of cancer gene classification table') + + args = parser.parse_args() +# make it a little easier to pass forward filename args +args_dict = vars(args) +filename_arg_list = [ 'x_matrix' ] +for k in args_dict.keys(): + if k.startswith('filename_'): + filename_arg_list.append(k) + +print(filename_arg_list) # Load command arguments +# if list of the genes provided by file or comma seperated values: genes = args.genes -diseases = args.diseases.split(',') +#genes_df = pd.read_table(genes) +#genes = genes_df['genes'].tolist() + +#diseases = args.diseases.split(',') +# if list of the diseases provided by file or comma seperated values: +diseases = args.diseases +diseases_df = pd.read_table(diseases) +diseases = diseases_df['diseases'].tolist() + folder = args.alt_folder alphas = args.alphas l1_ratios = args.l1_ratios remove_hyper = args.remove_hyper +#base_folder = os.path.join('classifiers', 'within_disease', +# genes.replace(',', '_')) base_folder = os.path.join('classifiers', 'within_disease', - genes.replace(',', '_')) - + genes[0]) if diseases == 'Auto': sample_freeze_file = os.path.join('data', 'sample_freeze.tsv') sample_freeze = pd.read_table(sample_freeze_file, index_col=0) @@ -68,10 +104,18 @@ alt_folder = os.path.join(base_folder, acronym) else: alt_folder = os.path.join(folder, acronym) + command = ['python', os.path.join('scripts', 'pancancer_classifier.py'), - '--genes', genes, '--diseases', acronym, '--drop', + '--genes', genes, '--diseases', acronym, '--drop', '--copy_number', '--alphas', alphas, '--l1_ratios', l1_ratios, '--alt_folder', alt_folder, '--shuffled', '--keep_intermediate'] if remove_hyper: command += ['--remove_hyper'] + + # Only set filename if it has been set + for fname_arg in filename_arg_list: + val = args_dict.get(fname_arg, None) + if val: + command += ['--%s' % (fname_arg), val] + print(command) subprocess.call(command)