{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import re\n", "import os\n", "import math\n", "from multiprocessing import Pool\n", "from tqdm import tqdm\n", "from scipy import stats\n", "## init\n", "mySpecie='Homo_sapiens'\n", "#prealigned_dir='/cellar/users/btsui/all_seq_snp/Homo_sapiens_all_merged_snp.TCGA.prealigned.pickle'\n", "targetted_align_dir='/cellar/users/btsui/all_seq_snp/Homo_sapiens_all_merged_snp.TCGA.pickle'\n", "manifest_dir='/cellar/users/btsui/Project/METAMAP/notebook/RapMapTest/XGS_WGS/./tcga_lgg_wgs_bams.df.wxs_rnaseq.pickle'\n", "\n", "targetted_df=pd.read_pickle(targetted_align_dir).loc[\"TCGA\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "all_UUIDs=targetted_df.index.get_level_values('Run_digits').unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "manifest_df=pd.read_pickle(manifest_dir)\n", "\n", "manifest_df['processed']=manifest_df.file_id.isin(all_UUIDs)\n", "\n", "uuid_barcode_mapDf=pd.read_csv('/cellar/users/andreabc/GDC_barcodes/uuid_barcode_map.txt',sep='\\t').set_index('file_id')\n", "\n", "manifest_df['sample_barcode']=uuid_barcode_mapDf.loc[manifest_df.file_id]['sample_barcode'].values\n", "\n", "m_data_category=manifest_df.data_category=='Raw Sequencing Data'\n", "m_experimental_strategy=manifest_df['experimental_strategy'].isin(['RNA-Seq','WXS'])\n", "\n", "manifest_df_sub=manifest_df[manifest_df['processed']&m_data_category&m_experimental_strategy]\n", "\n", "tmpVC=manifest_df_sub['sample_barcode'].value_counts()\n", "\n", "#identify data with both RNAseq and WXS\n", "with_both=tmpVC.index[tmpVC==2]\n", "\n", "\n", "manifest_df_sub['is_tumor']=manifest_df_sub['sample_barcode'].str.contains('TCGA-\\w+-\\w+-01')\n", "manifest_df_w_RNA_WXS=manifest_df_sub[manifest_df_sub.sample_barcode.isin(with_both)&manifest_df_sub['is_tumor']]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "inVcfDir='/data/cellardata/users/btsui/dbsnp/Homo_sapiens/All_20170710.f1_byte2_not_00.vcf.gz' \n", "vcfDf=pd.read_csv(inVcfDir,sep='\\t',header=None)\n", "vcfDf.columns=['Chr','Pos','RsId','RefBase','AltBase','','','Annot']\n", "vcfDf['Chr']=vcfDf['Chr'].astype(np.str)" ] }, { "cell_type": "code", "execution_count": 270, "metadata": {}, "outputs": [], "source": [ "#queryChr,querySite,refBase='17',7673803,'G','A' #TP53\n", "#8\t142877758\n", "#6, 31356729\n", "#G\tA\n", "#1\t237591774\n", "#\n", "queryChr,querySite,refBase='1',237591774,'C',\n", "m_chrom=(targetted_df.index.get_level_values('Chr')==queryChr)" ] }, { "cell_type": "code", "execution_count": 271, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ChrPosRsIdRefBaseAltBaseAnnot
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" ], "text/plain": [ "features ReadDepth \\\n", "Run_digits Chr Pos base \n", "08ce1dd9-3167-4fe4-8619-724727a32a36 1 14727 A 18 \n", " G 280 \n", " 630825 T 11 \n", " 630833 C 9 \n", " 833068 G 2 \n", "\n", "features AverageBaseQuality \n", "Run_digits Chr Pos base \n", "08ce1dd9-3167-4fe4-8619-724727a32a36 1 14727 A 28 \n", " G 32 \n", " 630825 T 32 \n", " 630833 C 34 \n", " 833068 G 34 " ] }, "execution_count": 272, "metadata": {}, "output_type": "execute_result" } ], "source": [ "targetted_df.head()" ] }, { "cell_type": "code", "execution_count": 273, "metadata": {}, "outputs": [], "source": [ "m_site=(targetted_df.index.get_level_values('Pos')==querySite)" ] }, { "cell_type": "code", "execution_count": 274, "metadata": {}, "outputs": [], "source": [ "hitDf=targetted_df[m_site&m_chrom]" ] }, { "cell_type": "code", "execution_count": 275, "metadata": {}, "outputs": [], "source": [ "hitDfResetDf=hitDf.reset_index()" ] }, { "cell_type": "code", "execution_count": 276, "metadata": {}, "outputs": [], "source": [ "hitDfResetDf['is_ref']=hitDfResetDf.base==refBase" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 277, "metadata": {}, "outputs": [], "source": [ "uuidToExperimentS=manifest_df_w_RNA_WXS.set_index('file_id')['experimental_strategy']" ] }, { "cell_type": "code", "execution_count": 278, "metadata": {}, "outputs": [], "source": [ "uuidToBarcodeS=manifest_df_w_RNA_WXS.set_index('file_id')['sample_barcode']" ] }, { "cell_type": "code", "execution_count": 279, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: \n", "Passing list-likes to .loc or [] with any missing label will raise\n", "KeyError in the future, you can use .reindex() as an alternative.\n", "\n", "See the documentation here:\n", "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n", " \"\"\"Entry point for launching an IPython kernel.\n", "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: \n", "Passing list-likes to .loc or [] with any missing label will raise\n", "KeyError in the future, you can use .reindex() as an alternative.\n", "\n", "See the documentation here:\n", "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n", " \n" ] } ], "source": [ "hitDfResetDf['Strategy']=uuidToExperimentS.loc[hitDfResetDf['Run_digits']].values\n", "hitDfResetDf['Barcode']=uuidToBarcodeS.loc[hitDfResetDf['Run_digits']].values" ] }, { "cell_type": "code", "execution_count": 280, "metadata": {}, "outputs": [], "source": [ "validHitResetDf=hitDfResetDf.dropna()" ] }, { "cell_type": "code", "execution_count": 281, "metadata": {}, "outputs": [], "source": [ "unstackDf=validHitResetDf.set_index(['Barcode','base','Strategy'])['ReadDepth'].unstack().unstack().fillna(0)#.set_index(['Strategy'])" ] }, { "cell_type": "code", "execution_count": 282, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 283, "metadata": {}, "outputs": [], "source": [ "#unstackDf" ] }, { "cell_type": "code", "execution_count": 284, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'ref at 1:237591774'" ] }, "execution_count": 284, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'ref at {}:{}'.format(queryChr,querySite)" ] }, { "cell_type": "code", "execution_count": 292, "metadata": {}, "outputs": [], "source": [ "altBase='A'" ] }, { "cell_type": "code", "execution_count": 293, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 293, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#refBase,altBase\n", "g=sns.jointplot(data=unstackDf['RNA-Seq'],x=refBase,y=altBase)#['G']#['RNA-Seq']\n", "g.set_axis_labels('ref allele read count at {}:{}'.format(queryChr,querySite),'alt allle')" ] }, { "cell_type": "code", "execution_count": 300, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 300, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g=sns.jointplot(x=np.log2(unstackDf['RNA-Seq'][altBase]+1),y=np.log2(unstackDf['WXS'][altBase]+1))\n", "g.set_axis_labels('Alt allele read count at {}:{} in RNAseq'.format(queryChr,querySite),\n", " 'Alt allele read count at {}:{} in WXS'.format(queryChr,querySite))" ] }, { "cell_type": "code", "execution_count": 295, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 295, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(data=unstackDf['WXS'],x='G',y='A')" ] }, { "cell_type": "code", "execution_count": 296, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'A'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3062\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3063\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3064\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'A'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0munstackDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'A'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2681\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2682\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_mi_columns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2683\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2684\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2685\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2726\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2727\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2728\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2729\u001b[0m \u001b[0mnew_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method)\u001b[0m\n\u001b[1;32m 2262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2263\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2264\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2266\u001b[0m \u001b[0;31m# _get_level_indexer returns an empty slice if the key has\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m 2521\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2523\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2524\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3063\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3064\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3065\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3067\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'A'" ] } ], "source": [ "unstackDf['A'].head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.jointplot(data=np.log2(unstackDf['A']+1),x=('RNA-Seq'),y=('WXS'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "unstackDf['WXS'].sum(axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "manifest_dmanifest_g=manifest_df_w_RNA_WXS.groupby(['sample_barcode','experimental_strategy'])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "file_id\n", "1985b367-00c4-4c25-b049-5858e937cc6d RNA-Seq\n", "a7aff6a7-cea4-42e9-8d56-b211843a0302 RNA-Seq\n", "4038984b-21d1-45d3-a5bb-208f9e71aa78 RNA-Seq\n", "7b17a5ef-c778-4753-8c30-427af9171f7a RNA-Seq\n", "93e20db7-4892-4ef8-9331-ecd665e4cb91 RNA-Seq\n", "35a18f9e-0ef8-4e55-b655-33397b63fd1a RNA-Seq\n", "bc27f71a-4222-4940-8a64-246e2b9f3d44 WXS\n", "84943493-e5ef-4099-8706-09afa625b076 WXS\n", "f36d778a-2c49-43aa-a577-7a6a921ad6cc WXS\n", "243e9ccb-7c93-4c6a-9e3e-105c379e7f78 RNA-Seq\n", "f895b46c-7811-4ebb-ac9c-970bbfed657a RNA-Seq\n", "3594993e-f65c-434b-a64e-b1873f4b04ca WXS\n", "4522becf-1566-4de8-b760-79bb93513ecf WXS\n", "65c95f19-84f0-4e8b-99c3-186f881181a4 RNA-Seq\n", "eb4dda76-4215-458b-b9a0-60f9ff24bd6e RNA-Seq\n", "f4db57f0-ba62-4a60-b65d-d2354612eb7b RNA-Seq\n", "00b53e00-d640-49e5-b2bd-3a3bdf867998 RNA-Seq\n", "e55c8de3-3a92-42f2-bc1e-70a21355d696 WXS\n", "3a0e5ae0-dc79-468d-b459-a6d43b612851 WXS\n", "3011867e-1c3b-4791-849e-4e7d636ddc88 RNA-Seq\n", "fd5d9171-979e-4742-adec-179f19bf6c06 WXS\n", "aeebf360-49c4-4db3-bdf4-daffdc5279cc RNA-Seq\n", "25650a4b-775b-485b-92c2-3f8f30ba4169 WXS\n", "b2bd014e-44fe-4d24-9b75-91baf36b3c0a RNA-Seq\n", "dcbaf670-8100-4c98-bd4d-2880a5805f23 RNA-Seq\n", "eda26d7b-0725-46c4-9264-bcdf94dc163d RNA-Seq\n", "e52ef468-33a1-4241-8286-be7387218f18 RNA-Seq\n", "06c8a70b-3f0c-42bc-875a-f3e13c887a73 RNA-Seq\n", "386d69e7-b4a7-4981-beb8-98f088c689f7 WXS\n", "37589627-0b38-41b0-b7a3-d2dd1f343550 RNA-Seq\n", " ... \n", "e2a550e9-4f8c-48ea-838d-2e4bcfcbbfe4 WXS\n", "4b5c011b-efe1-476c-91cf-fd2776ce80a5 WXS\n", "78be435a-35d5-4ef7-8ded-e323a9c342d1 WXS\n", "ff151b36-6dc1-4462-af67-0d5947d38df4 WXS\n", "5dbbebdc-a12b-4b04-8f4f-510f34e87e05 WXS\n", "c6bfb8ca-6824-4dac-a415-b2a676be4cc6 WXS\n", "6eff4d5c-1647-4505-9d70-f36e4a532124 WXS\n", "761633fb-a98a-4a62-973b-69a65e4da5a7 WXS\n", "bd2695cb-879e-4f57-865b-1289a036ac52 WXS\n", "419a5f50-4217-41b5-a534-9437c0be4747 WXS\n", "5d931e04-0e84-4b36-b555-e3d82bfc8e6f WXS\n", "71266893-25de-4a54-a1db-3cdafb34b44a WXS\n", "de3dfa71-a490-4344-95dc-99301d1dee08 WXS\n", "cdb99f45-9d8a-4f5f-8e10-9b3ef51eac2e WXS\n", "6bd2f0f6-df8f-4b4e-804b-328923ec382b WXS\n", "4e30cef6-d1af-4d77-ad93-6e31ec58fc35 WXS\n", "3772a453-c9cb-4084-b73a-181304516637 WXS\n", "5de82077-8540-474f-a569-c4b3b951f81c WXS\n", "704f5788-ca6e-499f-8d43-47134c5ba275 WXS\n", "2207155d-348a-4ed0-91f1-71b60f179ee2 WXS\n", "e45b1fae-1a83-438e-a6e3-967813831346 WXS\n", "67a2edc2-4776-4442-b74c-ff8b9b91867e WXS\n", "2d622f8a-a91d-44ed-a818-ea7c2fd698cd WXS\n", "06350102-0c44-4846-935d-515a5b0da989 WXS\n", "17e5b895-6ec1-4bfc-a55f-a2114adb0af1 WXS\n", "db95c082-fe8d-4c85-a4d8-b63d4a39c1ea WXS\n", "5b96100f-1815-453d-b2a4-b32bb747b4ad WXS\n", "d1ff8258-be02-44bf-9cbb-0bbc895452bf WXS\n", "e81ecc22-c399-4d35-b4b0-abcec7f895d5 WXS\n", "2b0048e0-a062-40d2-a1e1-4bb763ea0ead WXS\n", "Name: experimental_strategy, Length: 1048, dtype: object" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "manifest_df_w_RNA_WXS.set_index('file_id')['experimental_strategy']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": 2 }