{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bamToBed -i A2508.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > A2508.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i A375.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > A375.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i A549.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > A549.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i BxPC3.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > BxPC3.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i C42Bu.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > C42Bu.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i Colo320.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > Colo320.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i H1299.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > H1299.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i H23u.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > H23u.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i H358u.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > H358u.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HCC44.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HCC44.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HCT116.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HCT116.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HEK293T-16.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HEK293T-16.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HEK293T-1.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HEK293T-1.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HEK293T-26.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HEK293T-26.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HEK293T-9.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HEK293T-9.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HEPG2.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HEPG2.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i HS707A.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > HS707A.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i K562.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > K562.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i LNCaP.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > LNCaP.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i LS174T.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > LS174T.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i MCF7.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > MCF7.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i MDA231.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > MDA231.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i MDA453.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > MDA453.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i PACA2.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > PACA2.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i PANC1.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > PANC1.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i PC3.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > PC3.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i SW480u.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > SW480u.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i SW620u.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > SW620u.srt.dd.q10.oncRNA.cnt\n", "bamToBed -i ZR751.srt.dd.q10.bam | intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | cut -f10 | sort | uniq -c | awk '{ print t }' > ZR751.srt.dd.q10.oncRNA.cnt\n" ] } ], "source": [ "%%bash\n", "for f in *.dd.q10.bam; do\n", "out=${f/.dd.q10.bam/.dd.q10.oncRNA.cnt};\n", "echo \"\"\"bamToBed -i $f | \\\n", "intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | \\\n", "cut -f10 | sort | uniq -c | awk '{ print $2 \"\\t\" $1}' > $out\"\"\"\n", "bamToBed -i $f | \\\n", "intersectBed -wo -s -a stdin -b ../../exoRNA/exo-oncRNA_ISPY2_batch3/pancancer_oncRNA_annot.bed | \\\n", "cut -f10 | sort | uniq -c | awk '{ print $2 \"\\t\" $1}' > $out\n", "done" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import glob\n", "\n", "dataframes = []\n", "\n", "for fn in glob.glob(\"*.dd.q10.oncRNA.cnt\"):\n", " tmp = pd.read_csv(fn, sep=\"\\t\", header=None, index_col=0)\n", " tmp.columns = [fn.replace(\".srt.dd.q10.oncRNA.cnt\", \"\")]\n", " dataframes.append(tmp)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10178, 29)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_df = pd.concat(dataframes, axis=0).fillna(0)\n", "count_df.shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
linetissue typelib_sizeTCGA
smRNAseq
A2508A2058skin281609SKCM
A375A375skin331503SKCM
A549A549lung363346LUAD
BxPC3BxPC3pancreas358942PAAD
C42BuC42Bprostate343257PRAD
\n", "
" ], "text/plain": [ " line tissue type lib_size TCGA\n", "smRNAseq \n", "A2508 A2058 skin 281609 SKCM\n", "A375 A375 skin 331503 SKCM\n", "A549 A549 lung 363346 LUAD\n", "BxPC3 BxPC3 pancreas 358942 PAAD\n", "C42Bu C42B prostate 343257 PRAD" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meta = pd.read_csv(\"meta.tsv\", sep=\"\\t\", header=0, index_col=0)\n", "meta.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('A2508', 'A2058'), ('A375', 'A375')]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samp_line_dict = meta[\"line\"].to_dict()\n", "list(samp_line_dict.items())[:2]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
A375HEPG2MDA231PACA2LS174TPANC1H23uSW480uColo320BxPC3...A2508MDA453K562C42BuSW620uHEK293T-16MCF7HCT116HEK293T-1H1299
0
chr10:100373207-100373235:-1.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:103712417-103712525:+1.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:112950886-112950915:+1.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:113045362-113045392:+1.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:118959315-118959431:+1.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
\n", "

5 rows × 29 columns

\n", "
" ], "text/plain": [ " A375 HEPG2 MDA231 PACA2 LS174T PANC1 H23u \\\n", "0 \n", "chr10:100373207-100373235:- 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:103712417-103712525:+ 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:112950886-112950915:+ 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:113045362-113045392:+ 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:118959315-118959431:+ 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", " SW480u Colo320 BxPC3 ... A2508 MDA453 K562 \\\n", "0 ... \n", "chr10:100373207-100373235:- 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n", "chr10:103712417-103712525:+ 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n", "chr10:112950886-112950915:+ 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n", "chr10:113045362-113045392:+ 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n", "chr10:118959315-118959431:+ 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n", "\n", " C42Bu SW620u HEK293T-16 MCF7 HCT116 \\\n", "0 \n", "chr10:100373207-100373235:- 0.0 0.0 0.0 0.0 0.0 \n", "chr10:103712417-103712525:+ 0.0 0.0 0.0 0.0 0.0 \n", "chr10:112950886-112950915:+ 0.0 0.0 0.0 0.0 0.0 \n", "chr10:113045362-113045392:+ 0.0 0.0 0.0 0.0 0.0 \n", "chr10:118959315-118959431:+ 0.0 0.0 0.0 0.0 0.0 \n", "\n", " HEK293T-1 H1299 \n", "0 \n", "chr10:100373207-100373235:- 0.0 0.0 \n", "chr10:103712417-103712525:+ 0.0 0.0 \n", "chr10:112950886-112950915:+ 0.0 0.0 \n", "chr10:113045362-113045392:+ 0.0 0.0 \n", "chr10:118959315-118959431:+ 0.0 0.0 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_bin_df = count_df.clip(lower=0, upper=1)\n", "count_bin_df.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
smRNAseqA2508A375A549BxPC3C42BuColo320H1299H23uH358uHCC44...LS174TMCF7MDA231MDA453PACA2PANC1PC3SW480uSW620uZR751
0
chr10:100373207-100373235:-0.03.0165640.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:103712417-103712525:+0.03.0165640.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:112950886-112950915:+0.03.0165640.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:113045362-113045392:+0.09.0496920.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
chr10:118959315-118959431:+0.03.0165640.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
\n", "

5 rows × 29 columns

\n", "
" ], "text/plain": [ "smRNAseq A2508 A375 A549 BxPC3 C42Bu Colo320 \\\n", "0 \n", "chr10:100373207-100373235:- 0.0 3.016564 0.0 0.0 0.0 0.0 \n", "chr10:103712417-103712525:+ 0.0 3.016564 0.0 0.0 0.0 0.0 \n", "chr10:112950886-112950915:+ 0.0 3.016564 0.0 0.0 0.0 0.0 \n", "chr10:113045362-113045392:+ 0.0 9.049692 0.0 0.0 0.0 0.0 \n", "chr10:118959315-118959431:+ 0.0 3.016564 0.0 0.0 0.0 0.0 \n", "\n", "smRNAseq H1299 H23u H358u HCC44 ... LS174T MCF7 \\\n", "0 ... \n", "chr10:100373207-100373235:- 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "chr10:103712417-103712525:+ 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "chr10:112950886-112950915:+ 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "chr10:113045362-113045392:+ 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "chr10:118959315-118959431:+ 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "\n", "smRNAseq MDA231 MDA453 PACA2 PANC1 PC3 SW480u \\\n", "0 \n", "chr10:100373207-100373235:- 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:103712417-103712525:+ 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:112950886-112950915:+ 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:113045362-113045392:+ 0.0 0.0 0.0 0.0 0.0 0.0 \n", "chr10:118959315-118959431:+ 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "smRNAseq SW620u ZR751 \n", "0 \n", "chr10:100373207-100373235:- 0.0 0.0 \n", "chr10:103712417-103712525:+ 0.0 0.0 \n", "chr10:112950886-112950915:+ 0.0 0.0 \n", "chr10:113045362-113045392:+ 0.0 0.0 \n", "chr10:118959315-118959431:+ 0.0 0.0 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## calculate count per million\n", "cpm_df = count_df.mul(1e6).div(meta.loc[count_df.columns, \"lib_size\"]).loc[:, meta.index]\n", "cpm_df.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "count_df.columns = [samp_line_dict[x] for x in count_df.columns.tolist()]\n", "cpm_df.columns = [samp_line_dict[x] for x in cpm_df.columns.tolist()]\n", "count_bin_df.columns = [samp_line_dict[x] for x in count_bin_df.columns.tolist()]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "count_df.loc[:, meta.line].astype(int).to_csv(\"smRNA-seq_oncRNA_count.tsv\", sep=\"\\t\", header=True, index_label=\"oncRNA\")\n", "count_bin_df.loc[:, meta.line].astype(int).to_csv(\"smRNA-seq_oncRNA_bin.tsv\", sep=\"\\t\", header=True, index_label=\"oncRNA\")\n", "cpm_df.to_csv(\"smRNA-seq_oncRNA_cpm.tsv\", sep=\"\\t\", header=True, index_label=\"oncRNA\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intersect with cancer-specific TCGA calls" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ACCBLCABRCACESCCHOLCOADDLBCESCAHNSCKICH...READSARCSKCMSTADTGCTTHCATHYMUCECUCSUVM
chr10:100020517-100020620:+0.01.00.00.01.00.01.00.01.00.0...0.00.00.00.01.00.01.00.00.00.0
chr10:100056307-100056363:+0.00.00.00.01.00.00.00.00.00.0...0.00.00.00.01.00.00.00.00.00.0
chr10:100071521-100071550:-0.01.00.00.01.00.00.00.01.01.0...0.00.01.01.01.00.00.00.00.00.0
chr10:100092234-100092264:+0.01.00.01.00.01.00.01.01.00.0...0.00.01.00.01.00.00.00.00.01.0
chr10:100094212-100094264:-0.00.00.00.00.00.00.00.01.00.0...0.00.00.00.01.00.00.00.00.00.0
\n", "

5 rows × 32 columns

\n", "
" ], "text/plain": [ " ACC BLCA BRCA CESC CHOL COAD DLBC ESCA \\\n", "chr10:100020517-100020620:+ 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 \n", "chr10:100056307-100056363:+ 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "chr10:100071521-100071550:- 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "chr10:100092234-100092264:+ 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 \n", "chr10:100094212-100094264:- 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", " HNSC KICH ... READ SARC SKCM STAD TGCT \\\n", "chr10:100020517-100020620:+ 1.0 0.0 ... 0.0 0.0 0.0 0.0 1.0 \n", "chr10:100056307-100056363:+ 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.0 \n", "chr10:100071521-100071550:- 1.0 1.0 ... 0.0 0.0 1.0 1.0 1.0 \n", "chr10:100092234-100092264:+ 1.0 0.0 ... 0.0 0.0 1.0 0.0 1.0 \n", "chr10:100094212-100094264:- 1.0 0.0 ... 0.0 0.0 0.0 0.0 1.0 \n", "\n", " THCA THYM UCEC UCS UVM \n", "chr10:100020517-100020620:+ 0.0 1.0 0.0 0.0 0.0 \n", "chr10:100056307-100056363:+ 0.0 0.0 0.0 0.0 0.0 \n", "chr10:100071521-100071550:- 0.0 0.0 0.0 0.0 0.0 \n", "chr10:100092234-100092264:+ 0.0 0.0 0.0 0.0 1.0 \n", "chr10:100094212-100094264:- 0.0 0.0 0.0 0.0 0.0 \n", "\n", "[5 rows x 32 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "onc_call_df = pd.read_csv(\"/avicenna/hani/oncRNAs/pancancer_oncRNA_annot.csv\", index_col=0)\n", "#onc_call_df = onc_call_df.loc[onc_call_df.sum(axis=1)==1,:]\n", "onc_call_df.head()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "#populate the sets of tissue-specific oncRNAs\n", "sm_set_dict = {}\n", "\n", "for code_sm in meta.TCGA.unique():\n", " if pd.isna(code_sm):\n", " continue\n", " tmp = count_bin_df.loc[:,meta.query(f'TCGA==\"{code_sm}\"').index]\n", " tmp.sum(axis=1)>0\n", " sm_set_dict[code_sm] = set(tmp.index[tmp.sum(axis=1)>0].tolist()).intersection(set(onc_call_df.index.tolist()))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "tcga_set_dict = {}\n", "for code_tcga in meta.TCGA.unique():\n", " if pd.isna(code_tcga):\n", " continue\n", " tcga_set_dict[code_tcga] = set(onc_call_df.query(f'{code_tcga} == 1').index.tolist())" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SKCM SKCM 676 784 101296 261479 386.200164647297\n", "SKCM LUAD 270 784 67025 261479 17.90364727831407\n", "SKCM PAAD 33 784 32804 261479 8.490971304867709e-16\n", "SKCM PRAD 6 784 8562 261479 2.743710848406422e-06\n", "SKCM COAD 221 784 48071 261479 25.670667151056076\n", "SKCM LIHC 70 784 36813 261479 7.2809719508556e-06\n", "SKCM BRCA 95 784 15443 261479 24.652952784497856\n", "LUAD SKCM 1090 1485 101296 261479 374.18341102992633\n", "LUAD LUAD 656 1485 67025 261479 124.2042201453095\n", "LUAD PAAD 87 1485 32804 261479 7.054415790075996e-18\n", "LUAD PRAD 17 1485 8562 261479 1.0204905613034583e-07\n", "LUAD COAD 561 1485 48071 261479 158.80772125535117\n", "LUAD LIHC 228 1485 36813 261479 2.5997545813916894\n", "LUAD BRCA 222 1485 15443 261479 83.32036872800815\n", "PAAD SKCM 539 740 101296 261479 181.94632392984667\n", "PAAD LUAD 288 740 67025 261479 34.771518328261294\n", "PAAD PAAD 36 740 32804 261479 1.1717282188762453e-12\n", "PAAD PRAD 10 740 8562 261479 0.0008043618927956497\n", "PAAD COAD 282 740 48071 261479 83.19778526619156\n", "PAAD LIHC 107 740 36813 261479 1.025435108414527\n", "PAAD BRCA 107 740 15443 261479 39.34904826606989\n", "PRAD SKCM 572 853 101296 261479 144.34233978556276\n", "PRAD LUAD 367 853 67025 261479 64.60121803525338\n", "PRAD PAAD 35 853 32804 261479 1.5792493355732288e-17\n", "PRAD PRAD 29 853 8562 261479 0.9916755555505878\n", "PRAD COAD 321 853 48071 261479 91.30469150623613\n", "PRAD LIHC 138 853 36813 261479 3.3048682289775737\n", "PRAD BRCA 145 853 15443 261479 68.93984353745343\n", "COAD SKCM 1329 1868 101296 261479 410.00065822345493\n", "COAD LUAD 815 1868 67025 261479 147.06765731283073\n", "COAD PAAD 106 1868 32804 261479 2.2650481465317757e-23\n", "COAD PRAD 22 1868 8562 261479 4.463368190919048e-09\n", "COAD COAD 844 1868 48071 261479 359.57574902903997\n", "COAD LIHC 271 1868 36813 261479 1.2616334715770203\n", "COAD BRCA 264 1868 15443 261479 88.54864279048729\n", "LIHC SKCM 158 220 101296 261479 53.37767536816502\n", "LIHC LUAD 75 220 67025 261479 6.191017709128136\n", "LIHC PAAD 7 220 32804 261479 1.1111909303347377e-06\n", "LIHC PRAD 0 220 8562 261479 0.0006574350797109173\n", "LIHC COAD 86 220 48071 261479 29.238309564327583\n", "LIHC LIHC 41 220 36813 261479 3.733073240959083\n", "LIHC BRCA 26 220 15443 261479 8.190524136316517\n", "BRCA SKCM 742 1039 101296 261479 233.07313061931674\n", "BRCA LUAD 463 1039 67025 261479 91.22314278313954\n", "BRCA PAAD 57 1039 32804 261479 1.9219419672297858e-14\n", "BRCA PRAD 27 1039 8562 261479 0.13381003867446317\n", "BRCA COAD 376 1039 48071 261479 96.01582977835838\n", "BRCA LIHC 141 1039 36813 261479 0.41227075674790575\n", "BRCA BRCA 205 1039 15443 261479 120.01572909715183\n" ] } ], "source": [ "from collections import defaultdict\n", "from scipy.stats import hypergeom\n", "\n", "pv_dict = defaultdict(dict)\n", "\n", "for code_sm in meta.TCGA.unique():\n", " if pd.isna(code_sm):\n", " continue\n", " for code_tcga in meta.TCGA.unique():\n", " if pd.isna(code_tcga):\n", " continue\n", " pv = -hypergeom.logsf( \n", " len(sm_set_dict[code_sm].intersection(tcga_set_dict[code_tcga]))-1,\n", " onc_call_df.shape[0],\n", " len(sm_set_dict[code_sm]),\n", " len(tcga_set_dict[code_tcga])\n", " )\n", " pv_dict[code_sm][code_tcga] = pv\n", " print(code_sm, code_tcga, len(sm_set_dict[code_sm].intersection(tcga_set_dict[code_tcga]))-1, len(sm_set_dict[code_sm]), len(tcga_set_dict[code_tcga]), onc_call_df.shape[0], pv)\n", " " ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SKCMLUADPAADPRADCOADLIHCBRCA
SKCM3.862002e+023.741834e+021.819463e+021.443423e+024.100007e+0253.3776752.330731e+02
LUAD1.790365e+011.242042e+023.477152e+016.460122e+011.470677e+026.1910189.122314e+01
PAAD8.490971e-167.054416e-181.171728e-121.579249e-172.265048e-230.0000011.921942e-14
PRAD2.743711e-061.020491e-078.043619e-049.916756e-014.463368e-090.0006571.338100e-01
COAD2.567067e+011.588077e+028.319779e+019.130469e+013.595757e+0229.2383109.601583e+01
LIHC7.280972e-062.599755e+001.025435e+003.304868e+001.261633e+003.7330734.122708e-01
BRCA2.465295e+018.332037e+013.934905e+016.893984e+018.854864e+018.1905241.200157e+02
\n", "
" ], "text/plain": [ " SKCM LUAD PAAD PRAD COAD \\\n", "SKCM 3.862002e+02 3.741834e+02 1.819463e+02 1.443423e+02 4.100007e+02 \n", "LUAD 1.790365e+01 1.242042e+02 3.477152e+01 6.460122e+01 1.470677e+02 \n", "PAAD 8.490971e-16 7.054416e-18 1.171728e-12 1.579249e-17 2.265048e-23 \n", "PRAD 2.743711e-06 1.020491e-07 8.043619e-04 9.916756e-01 4.463368e-09 \n", "COAD 2.567067e+01 1.588077e+02 8.319779e+01 9.130469e+01 3.595757e+02 \n", "LIHC 7.280972e-06 2.599755e+00 1.025435e+00 3.304868e+00 1.261633e+00 \n", "BRCA 2.465295e+01 8.332037e+01 3.934905e+01 6.893984e+01 8.854864e+01 \n", "\n", " LIHC BRCA \n", "SKCM 53.377675 2.330731e+02 \n", "LUAD 6.191018 9.122314e+01 \n", "PAAD 0.000001 1.921942e-14 \n", "PRAD 0.000657 1.338100e-01 \n", "COAD 29.238310 9.601583e+01 \n", "LIHC 3.733073 4.122708e-01 \n", "BRCA 8.190524 1.200157e+02 " ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame.from_dict(pv_dict)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from matplotlib.patches import Rectangle\n", "\n", "import matplotlib\n", "matplotlib.rcParams['pdf.fonttype'] = 42\n", "matplotlib.rcParams['ps.fonttype'] = 42\n", "\n", "pv_df = pd.DataFrame.from_dict(pv_dict)\n", "pv_df.drop(\"LIHC\", axis=1, inplace=True)\n", "pv_df.drop(\"LIHC\", axis=0, inplace=True)\n", "indices = [\"BRCA\", \"LUAD\", \"PAAD\", \"PRAD\", \"COAD\", \"SKCM\"]\n", "pv_df = pv_df.div(pv_df.sum(axis=1), axis=0)\n", "pv_df = pv_df.div(pv_df.sum(axis=0), axis=1)\n", "pv_df = pv_df.loc[indices, indices]\n", "g = sns.clustermap(pv_df, row_cluster=False, col_cluster=False)\n", "#g = sns.clustermap(pv_df)\n", "\n", "ax = g.ax_heatmap\n", "for i in range(6):\n", " ax.add_patch(Rectangle((5-i, 5-i), 1, 1, fill=False, edgecolor='gold', lw=2))\n", "ax.set(xlabel='TCGA', ylabel='cell lines')\n", "\n", "plt.savefig('smRNA_vs_TCGA_code_overlap.pdf')\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SKCMLUADPAADPRADCOADBRCA
SKCM0.6363742.497096e-010.0775550.0607791.986119e-010.156644
LUAD0.1063632.988375e-010.0534370.0980732.568537e-010.221042
PAAD0.0020316.832563e-060.7248820.0000101.592465e-110.018747
PRAD0.0000071.045902e-070.0005270.6413013.320584e-090.138115
COAD0.0898242.250479e-010.0753070.0816413.698825e-010.137031
BRCA0.1654022.263980e-010.0682930.1181961.746519e-010.328421
\n", "
" ], "text/plain": [ " SKCM LUAD PAAD PRAD COAD BRCA\n", "SKCM 0.636374 2.497096e-01 0.077555 0.060779 1.986119e-01 0.156644\n", "LUAD 0.106363 2.988375e-01 0.053437 0.098073 2.568537e-01 0.221042\n", "PAAD 0.002031 6.832563e-06 0.724882 0.000010 1.592465e-11 0.018747\n", "PRAD 0.000007 1.045902e-07 0.000527 0.641301 3.320584e-09 0.138115\n", "COAD 0.089824 2.250479e-01 0.075307 0.081641 3.698825e-01 0.137031\n", "BRCA 0.165402 2.263980e-01 0.068293 0.118196 1.746519e-01 0.328421" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pv_df" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SKCM 676 784 101296 1.885382985704475e-168\n", "LUAD 656 1485 67025 1.1449659245774166e-54\n", "PAAD 36 740 32804 1.0\n", "PRAD 29 853 8562 0.3709546149419881\n", "COAD 844 1868 48071 6.890272063908644e-157\n", "LIHC 41 220 36813 0.02391921325879815\n", "BRCA 205 1039 15443 7.547986444002562e-53\n" ] } ], "source": [ "from collections import defaultdict\n", "from scipy.stats import hypergeom\n", "\n", "with open(\"smRNA-seq_TCGA_by_code_overlap.tsv\", \"wt\") as out:\n", " for code in meta.TCGA.unique():\n", " if pd.isna(code):\n", " continue\n", " \n", " pv = hypergeom.sf( \n", " len(sm_set_dict[code].intersection(tcga_set_dict[code]))-1,\n", " onc_call_df.shape[0],\n", " len(sm_set_dict[code]),\n", " len(tcga_set_dict[code])\n", " )\n", " print(code, len(sm_set_dict[code].intersection(tcga_set_dict[code]))-1, len(sm_set_dict[code]), len(tcga_set_dict[code]), pv)\n", " out.write(f\"{code}\\t{len(sm_set_dict[code].intersection(tcga_set_dict[code]))-1}\\t{len(sm_set_dict[code])}\\t{len(tcga_set_dict[code])}\\t{pv}\\n\")" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/avicenna/hani/tRNA-ENCODE/smRNA-seq\n" ] } ], "source": [ "!pwd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Draw PCA" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import rpy2.ipython\n", "%load_ext rpy2.ipython\n", "import warnings\n", "from rpy2.rinterface import RRuntimeWarning\n", "warnings.filterwarnings(\"ignore\", category=RRuntimeWarning)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A2508 A375 A549 BxPC3 C42Bu Colo320 H1299 H23u H358u\n", "chr1:107690458-107690658:- 1 0 0 0 0 0 0 0 0\n", " HCC44 HCT116 HEK293T.1 HEK293T.16 HEK293T.26\n", "chr1:107690458-107690658:- 0 0 0 0 0\n", " HEK293T.9 HEPG2 HS707A K562 LNCaP LS174T MCF7 MDA231\n", "chr1:107690458-107690658:- 0 0 0 0 0 0 0 0\n", " MDA453 PACA2 PANC1 PC3 SW480u SW620u ZR751\n", "chr1:107690458-107690658:- 0 0 0 0 0 0 0\n" ] } ], "source": [ "%%R\n", "files<-list.files(pattern=\".oncRNA.cnt\")\n", "datalist = lapply(files, function(x){read.table(file=x,header=FALSE,col.names=c(\"oncRNA\", sub(\".srt.dd.q10.oncRNA.cnt\", \"\", x)))})\n", "tmp <- Reduce(function(...) merge(..., by=1, all = TRUE), datalist)\n", "rownames(tmp) <- tmp[,1]\n", "tmp[is.na(tmp)] <- 0\n", "tmp <- tmp[,-1]\n", "head(tmp, n=1)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " line tissue.type lib_size TCGA\n", "A2508 A2058 skin 281609 SKCM\n", "A375 A375 skin 331503 SKCM\n", "A549 A549 lung 363346 LUAD\n", "BxPC3 BxPC3 pancreas 358942 PAAD\n", "C42Bu C42B prostate 343257 PRAD\n", "Colo320 Colo320 colon 457239 COAD\n" ] } ], "source": [ "%%R \n", "meta <- read.table(file=\"meta.tsv\", sep=\"\\t\", header=T, row.names=1)\n", "head(meta)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "%%R\n", "rownames(meta) <- gsub(\"-\", \".\", rownames(meta))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 6512 25\n" ] } ], "source": [ "%%R\n", "count.df <- tmp[,rownames(meta)]\n", "colnames(count.df) <- meta$line\n", "count.df <- count.df[,!grepl(\"HEK\", colnames(count.df))]\n", "\n", "dim(count.df)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Loading required package: S4Vectors\n", "\n", "R[write to console]: Loading required package: stats4\n", "\n", "R[write to console]: Loading required package: BiocGenerics\n", "\n", "R[write to console]: Loading required package: parallel\n", "\n", "R[write to console]: \n", "Attaching package: ‘BiocGenerics’\n", "\n", "\n", "R[write to console]: The following objects are masked from ‘package:parallel’:\n", "\n", " clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n", " clusterExport, clusterMap, parApply, parCapply, parLapply,\n", " parLapplyLB, parRapply, parSapply, parSapplyLB\n", "\n", "\n", "R[write to console]: The following objects are masked from ‘package:stats’:\n", "\n", " IQR, mad, sd, var, xtabs\n", "\n", "\n", "R[write to console]: The following objects are masked from ‘package:base’:\n", "\n", " anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n", " dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n", " grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n", " order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n", " rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n", " union, unique, unsplit, which, which.max, which.min\n", "\n", "\n", "R[write to console]: \n", "Attaching package: ‘S4Vectors’\n", "\n", "\n", "R[write to console]: The following object is masked from ‘package:base’:\n", "\n", " expand.grid\n", "\n", "\n", "R[write to console]: Loading required package: IRanges\n", "\n", "R[write to console]: Loading required package: GenomicRanges\n", "\n", "R[write to console]: Loading required package: GenomeInfoDb\n", "\n", "R[write to console]: Loading required package: SummarizedExperiment\n", "\n", "R[write to console]: Loading required package: Biobase\n", "\n", "R[write to console]: Welcome to Bioconductor\n", "\n", " Vignettes contain introductory material; view with\n", " 'browseVignettes()'. To cite Bioconductor, see\n", " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", "\n", "\n", "R[write to console]: Loading required package: DelayedArray\n", "\n", "R[write to console]: Loading required package: matrixStats\n", "\n", "R[write to console]: \n", "Attaching package: ‘matrixStats’\n", "\n", "\n", "R[write to console]: The following objects are masked from ‘package:Biobase’:\n", "\n", " anyMissing, rowMedians\n", "\n", "\n", "R[write to console]: Loading required package: BiocParallel\n", "\n", "R[write to console]: \n", "Attaching package: ‘DelayedArray’\n", "\n", "\n", "R[write to console]: The following objects are masked from ‘package:matrixStats’:\n", "\n", " colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges\n", "\n", "\n", "R[write to console]: The following objects are masked from ‘package:base’:\n", "\n", " aperm, apply, rowsum\n", "\n", "\n", "R[write to console]: converting counts to integer mode\n", "\n", "R[write to console]: factor levels were dropped which had no samples\n", "\n", "R[write to console]: estimating size factors\n", "\n", "R[write to console]: estimating dispersions\n", "\n", "R[write to console]: gene-wise dispersion estimates\n", "\n", "R[write to console]: mean-dispersion relationship\n", "\n", "R[write to console]: final dispersion estimates\n", "\n", "R[write to console]: fitting model and testing\n", "\n" ] } ], "source": [ "%%R\n", "library(DESeq2)\n", "colData <- meta[meta$line %in% colnames(count.df),]\n", "rownames(colData) <- colData$line\n", "dds <- DESeqDataSetFromMatrix(countData = count.df, colData=colData, design = ~ tissue.type)\n", "dds <- DESeq(dds)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: found already estimated dispersions, replacing these\n", "\n", "R[write to console]: gene-wise dispersion estimates\n", "\n", "R[write to console]: mean-dispersion relationship\n", "\n", "R[write to console]: final dispersion estimates\n", "\n" ] } ], "source": [ "%%R\n", "cds <- estimateDispersions(dds)\n", "vsd <- varianceStabilizingTransformation(cds)\n", "z <- plotPCA(vsd,intgroup=c(\"tissue.type\"), returnData=TRUE)\n", "percentVar <- round(100 * attr(z, \"percentVar\"))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(ggplot2)\n", "library(ggrepel)\n", "\n", "g <- ggplot(z, aes(PC1, PC2, color=tissue.type, label=name)) +\n", " geom_point(size=1.5) +\n", " xlab(paste0(\"PC1: \",percentVar[1],\"% variance\")) +\n", " ylab(paste0(\"PC2: \",percentVar[2],\"% variance\")) +\n", " geom_text_repel(size = 2) +\n", " coord_fixed() + theme_bw(11) + theme(panel.background = element_rect(colour = \"black\"), panel.grid.major = element_blank(), panel.grid.minor = element_blank())\n", "\n", "ggsave(\"oncRNA_PCA.pdf\", width=4, height=3)\n", "g" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "%%R\n", "by.line <- list()\n", "for (tissue in unique(meta$tissue.type)){\n", " if (tissue==\"kidney\"){\n", " next\n", " }\n", " sel <- meta$line[meta$tissue.type==tissue]\n", " if (length(sel) > 1){\n", " by.line[[tissue]] <- rowSums(count.df[,as.character(meta$line[meta$tissue.type==tissue])])\n", " }else{\n", " by.line[[tissue]] <- count.df[as.character(meta$line[meta$tissue.type==tissue])]\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " skin lung pancreas prostate colon liver bone blood\n", "chr1:107690458-107690658:- 1 0 0 0 0 0 0 0\n", "chr1:112707236-112707287:+ 2 1 1 1 3 0 1 0\n", "chr1:113904831-113904859:- 2 0 0 0 0 0 0 0\n", "chr1:11934566-11934593:- 1 0 0 0 0 0 0 0\n", "chr1:144552216-144552266:+ 4 1 0 0 2 0 0 0\n", "chr1:149888670-149888702:+ 1 0 0 0 0 0 0 0\n", " breast\n", "chr1:107690458-107690658:- 0\n", "chr1:112707236-112707287:+ 4\n", "chr1:113904831-113904859:- 0\n", "chr1:11934566-11934593:- 0\n", "chr1:144552216-144552266:+ 0\n", "chr1:149888670-149888702:+ 0\n" ] } ], "source": [ "%%R \n", "count.df.t <- as.data.frame(do.call(cbind, by.line), colnames=names(by.line))\n", "colnames(count.df.t) <- names(by.line)\n", "head(count.df.t)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 6512 9\n" ] } ], "source": [ "%%R\n", "count.df.bin <- count.df.t\n", "count.df.bin[count.df.bin>0] <- 1\n", "dim(count.df.bin)" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Saving 6.67 x 6.67 in image\n", "\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAMAAABKCk6nAAACcFBMVEUVFRUbGxscHBwdHR0pKSksLCwxMTEzMzM1NTU4ODg5OTk6Ojo7Ozs8PDw+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////NwwpJAAAb00lEQVR4nO2di38URZ7Avdt7b3v7cI9b3T0Q5UQQUBeBABFdFQkygsoC4SUhiclAMgKKCMrKSbK6RkQQEkFQHhuWXUhi9BjIgJzRDQ6vpP6l66qumumu6Uf1TD+qa37fz4f8TPdvKtX9dbqru39TcwcClOaOuDsAhAsIVhwQrDggWHFAsOKAYMWRVXB2d9w9UARZBQ+0xt0DRQDBigOCFQcEKw4IVhwQrDggWHFAsOKAYMWRWPDhTAahU+s29rQ1xd2bBCOx4KPX6xFqHNuyErVdibs7yUViwWj/gtuoEe3Ztnn+YNzdSS4SCz6N2rOoCW39Ar1yLe7uJBeJBXdt2dQ9dDKdOZLeEXdvEozEgoEgAMGKA4IVBwQrDghWHLkFa5i4u5JsQLDigGDFAcGKkwDBPekmhHramjrXtcTdqwQim+DezRu/w9EkePfYjkuoCbWt6uiPt2+JRDbBx2784TiO5kN03+oxtH/z/O2X62/G27kkIptg1Jv6GgeT4BPbbiHUh145jnZdjbdvSUQ2wR+j/j0IzRo/vSj4pZbWjqEj6R0H21+Pu3cJRDbBx9vTV/T3658Pwig6GGQTzIDLpIAAwYoDghUHBCtOMgSD6LIBwYoDghUHBCsOCFYcEKw4CRJMngvvbX78rdZMzJ1LEgkSTJ4Lo9HMpgsrx2LuXYJIkGDyXBh9ONi1dsVozL1LEAkSTJ4Lo2bUgN68FG/nkkSCBJPnwjea0aEMFMWLkyDBMfcooYBgxQHBipNEweyCGKpoBUiiYHpB/AL2/OWGFphmyY1kCabRuCDejD033OxvgGmWXEiiYHZBjD0v2960H6ZZciGJgukFMfG8Gu3qgWmWXEiiYArxfKyhDaZZciPBggERki+4hw6mydylAE/yBe+mg+lP8dylAE/yBbPBNJm7FOBJpmCzaDaY3ofnLgV4ki248BRRH0x3bdkUZ4dlJfmC4+xlAgDBigOCFQcEKw4IVhwlBNteHgMEEKw4IFhxQLDigGDFAcGKA4IVBwQrDghWHBCsOCBYcUCw4oBgxQHBigOCFUcdwaQA/nAmcxym0TKjjmBSAH/0ej1Mo2VBHcHGp4b3L3gfptEyo45gUgB/GrUvhGm0zKgjmBTAd23ZBNNoWVBHcIydlRkQrDggWHFAsOKAYMVRSjCILgUEKw4IVhwQrDggWHFAsOKAYMUBwYoDghUHBCsOCFYcEKw4IFhxQLDigGDFAcGKA4IVR0HB5DMst14diK/zMqGgYPIZloHUHuz51LqNVf7NdwoKNj7D0rEJe24c27Kyur/5TkHBxjffdQxgz41oz7bq/uY7BQWTz7Cgjvew5ya09Yvq/uY7BQVTiOeT6UyVf/OduoIBAghWHBCsOCBYcZQUDKKLKC2YRHLTsqetqUrvaKkveCA1gK+H25ZV5x0t9QWjDl3w/s3zt1bnHa2oBY9dFpvEKmDBfeiV09V5RytSwd9veHjKnCkPr//eOzVQwd1DR9I7qvSOVqSCN3xJwuAG79QABVc3sp2DB1qbLxgR/wTBFSOb4K5rF3biCIIDImLB+XNnf3DPuLJRfwc/P/e3IDgYohXcPG7evF+1uGWc2zqi/zz84e9BcDBEK3gC/vGwW8aG5pZPcQz0EF3NnqMVPPGdLwf3ThHJBMEBEa3gb1+eOfPlb0UyQXBAyDaKZoDggADBihOt4HH/8AsdkUwQHBDRCr75gGgmCA6IiA/Rl0UTAxdMnvsfb81U24eWIhY8onNLJDFwweS5/6YLK/tTIDhE/unv7v7Rv979V+/E4A/R+LFw19oVox0gOEQmjaHR+/qf9U4MR3ADevMSCA6TO4/f/OLfDjZ7J4YhuHsIf+UOCA6T/jnjZp5772/eiTCKDoiob3R8c1KstBEEB0TEglf+7Mm7VogkguCAiFjwT/V//yGSGJpgch28t/nx1zLV8S20EQv+966r+34qkhiaYHI9jEYzR6/XB7tpkhKx4P99+v6nvhZJDEUwiWQU/eEg2r/gdvDbJx+RCj5gIJIasuBm/EW02eC3UD4iFdxgIJIapuDuoRvNqGvLphC2UD6q6nmw+VxcLVStYDKa/nJDiz6g7o942yIlUsGfcdGF0AWT0XTDzf7RUbUvlyIV/MGTz7V3tj/3xB+9U8M/ROPB1rLtTXhArTIRH6KHDrx2YEgkMRrBq9GunMCTjyRTjedgYzEeTR9raLsBgmMhfMFVQtSC+/pvCOVFJpiNpvOPHyJRtZqtiAX/7sHaZoGPf0comI2mD957iMQ+xWq2IhY8AdWi6SKJ0R2i2Wh6xSEjKlbxEbHgu/c/svc/RRKjFYxH01gwjiC4EnJLpi0Tqo2OQrAR6WhafwcbEQRXwiNf4reJANEJVpyIBY+7px3ViCSC4ICIWPBjo2vuu1ckMTbB5DKpc10LuiD0WFN6Ihb8BkKnfy2SGJtgctnU1tGPWhsD2N74iVbwnn0Ykcz4DtF4kPXJ5fqDn6txDzNaweuaMSKZUQq2Riz4DNo1Z2uN0FMR2Yn8XvTQN0JpcQruHjrY/jqu21KBSAW/dw966o4fS3Or0ilijFvSFxoSP414pIKn/nD1Hy+i+KZw8CHYKJ9ubdz46torWPTRueuROR5vTUghSKSCx6M39bfvXSKpcQsmp+Kez5t3Tp05iEWvfnrxmDluurBSbOLruIlU8JTl//LVrS3jRVKlELx4a013x8prWHRX7eJRS1y7YjSw/RImkQrO7ziBvl0+LJIqgeBufRTdfOSZZiK6ruOVS+a46FU96odqfEtEaqq5osMrnUBFH1pUb4m7H8IfbWptxLdEpAYEewh2gp6jP7lcfzPwjQ+SqAXfuH79tkheIgTrh+r9aNdVn7sgWiIWXDv+wQdfFElMgGByqCa3RGQmYsGLRBOlF8xgd0SEXxAxEQuev75LvocNHukeHaV3RPzshSiJWHACHjYIp1PoaKviXRMSVTXLTmiCF8v76KmqZtkJSTAZbVW8a0KiqmbZCSddbqpqlh0QHDoSzLIDgkNEzpqsAASTq+HDmUxPWrb6gIhrslbpV0krRTITJphcDR+9Xr97bMelAPZTgEQruP1n7e1tPxfJTJBgEsgHXvYvuN23WrI6gGgFd07r7Ow8IZKZRMGnUfveBecR0o/T8lT0RDzIemc5Sr0tkpg8wd1DXVs2PTul/irSj9MvSlPRE7Hg+0eRPJ8PDjidQA7Vfas/kKaiJ+obHZ9eP6badXCJ4BPbbuEvh6h0XwVDxIIH5/9ydp9IYnIFdw+91NL6bqa10l0VENU8CUsYginkulgfbB2sXU/i4dimH6/mSVjCSTe6j6+L9cFW5rklJL4b2/Tj1TwJS4iC2WBrbO2s2yTGNv14NU/CErZgfbB1umNdlsRbtUfIsbqy3VcGMAlLWILJYGtn3WojvjzVOGZHPraOWHBP95F794okJlcwW8xhHLNXZQbICCy6O10RC554edylh0USlRR8Ytt5PPjSR2DRfXYtYsH/ve++byeLJCoo2DhWD5D/ju6zaxEL/ryub9dHIonKCaZ0GIKju9MVreBrz4hmqiu4e0j/cSiyO10Rv4Pve+F95So6BNNjAgrfQXCgKHwv2odgcqta/xHB7ONwLzrqdLJ19GppIPzZx+FedByC2WA6grmL4V50LOlU8K26QwidWreRTM5kigHe6IpYcN+Sacv6RBKVF0yulgZm6oIbx7YsxZMzmeK64G50QdlsXOkE/E5uRHsyeHImcwzuRheUzcYuuAltfQlPzmSOwd3ogklYYhbcPXQynSGTM5nigdp6ci4OYGoImIQlVsFOW48vn/Rz8feVTw0Bk7DIKJidm3dVPjUETMISc7o99Nw8v/KpIeBetKSCybkYTw1Bb2eWe1czUsE1BiKpVS7YvCeM25nl3tWMVHCvgUhq1Qh2SitCb2eWeVcTJiOVNL0ICE6GMRBsAQTbgG99oJ62pvT0mbbRbnZyECx5uhl860O/emqbvvdl27jKZnZy2QSPdXeSCILtBKM9X6P9m+fPuzx3wC5ut5mdXDbBl9pBsKPgJrT1O9SHXkmhZwbt4nGb2cllE4wGsODVi5aA4FLBJ9OZ7qEj6R3b5j5hG+1mJ5dT8Ae/fx0E226iO2wUZlokp2A4RJcnmI3CTFM2SyeYAoLLEkxHYYPFRSA4Geligtko7FpxEQhORrqYYDYKMy0CwclIF221BBCcjHQQHLsCEOwLEAyCkVwKQLAvQHBFm1gEBCcjHQTHrgAE+wIEg2AklwIQ7AsQDIKRXApAsC9AMAhGcikAwb4AwSAYyaUABPsCBINgJJcCEOwLEAyCkVwKQLAvQDAIRnIpAMG+AMEgGMmlAAT7AgSDYCSXAhDsCxAMgpFcCkCwL0AwCEZyKQDBvgDBIBjJpQAE+wIEg2AklwIQ7AsQDIKRXApAsC9AMAhGcikAwb4AwSAYyaUABPsCBINgJJcCEOwLEAyCkVwKQLAvQDAIRnIpAMG+AMEgGMmlAAT7AgSDYCSXAhDsCxAMgpFcCkCwL0AwCEZyKQDBvgDBIBjJpQAE+wIEg2AklwIQ7AsQDIKRXApAsC9AMAhGcikAwb4AwSAYyaUABPsCBINgJJcCEOwLEAyCkVwKQLAvQDAIRnIpAMG+AMEgGMmlAAT7AgSDYCSXAhDsCxAMgpFcCkCwL0AwCEZyKQDBvgDBIBjJpQAE+wIEg2AklwIQ7AsQDIKRXApAsC9AMAhGcikAwb4AwSAYyaUABPsCBINgJJcCECzE4O+WD+MIghUVvPmHk504gmBFBW9AudcQ2ta4atwinb/HeETxtCSn+2x10aJGukNlE9zy3fnOomCgbGQVPLim3nQOBipGNsEMEBwQIFhxpBV89hZQEXRHSis4D1TEDbojZRWM4t5BSQcEK44MgmvdVhrdzGazbjFbjJbVmqY5pIu0KlW6RrfFZ6sJEZylBt2izWKN7hXBdIcomFZOunireZdNcWldDsFz5t595tNJEy+mXn7i9PEZj/RZVooIzjrsW05w1iPdrdW40zW6LT5blUPwo7mLqSdunEqnzp1Oz1m+8HnLygAFe6W7tR74O9hnJzTtxyKC+dalEfzight/ak4N96YfHRk+Z1mZLxi2OQB5RLNggXTBtHDTnTYRC/aRzqIUglMzJ/y1e/J92dTwmR1HH5ybtazMAwa1c8t6mQyCXQl4NyUXEKw4IFhxVBUMUBbMr+jlIFh2QLDiJFhw3UiMfzwxJFpwQ/Had/SmXUpOh0buV6+IA6sh9Uj30WqI6U59rZntZxOlKputG0kNT0GNn+H70Klp520ycqadZBNz5miTxoqGPdLcWo8snetrMU0XLL6JmnSC1/TOQvg+dKrXLsOPYOed5pXms9Vw0u3+Z+QFe2+iVmI4bsF9T7+I8H3o1LBdhsfOQh6rFXgHs0O04CbK9w5G/3wW4fvQ9oLZmYaPOedoSXc+B/tsPYJ0ZoZfXXoOdttEqQQDZv6kfWa73GMU/Zb2N8vvNU9Z14NgWQDBigOCFafaBAf8TEZ+PtcO2y73eJr0hnbF8vvsJ40o/dOkQHZakgDBiqOeYFwWbX7eMLjOvJb2l5aQZQsx7xpLCt7d0/2l8en0j3mle6XR9R9r79umz5llm85q+9uoYLb6N/ONmAzBbkWi+dLoUfDuVplYRrrpMwdeaS4lnoXVumDb9Dmz7NILaW3aeUuffzNfmqpKXPdeN4Ir3/G/9yc94E+wTY2xm2D/Jcle6eaSdJd0typ8D8EkTRdsk24nmKTpgrOSCMZ173UjuPId/5s19hEIVk7wi3UjuPId/6sZ+xgO0WodonHde90IrnzH/96b/FiLeW0+TxUz0+ZfHWO5gyyf6ZY/5tWqpg24pV0tGWRZ/wo3yBpKziDLnbz8bLpTKK1Dc52u4Kq2nUR2mbSbCmZwl0lD2i4Se7RjJEp8meSO0L6LFxBcCUL7Ll5AsOJk7hRK+6OWdVt9TdtJInvY8K72jWU997DhW+0dEo9rp0hM7MOGBACCFQcEKw4IrhzBAjZWalZhWZxHjR5XFoca77T8cadOvKWdtmudpQ9q7WT1J1oXidu1v1jSi0V3lk7s0w6QmNEGLH98Zm3OsgclEWz/uQbRslnHklP3SlWPElSP1SinCzYtd+yELtilwDWnCyZpumASdcGWdGvZbGGxLpgs1wVb6qPlEPzE4hl/Pjp3Wg7Pr3No6qxbqWk7ZzzSh5e885R5ciURY7liubdHmiXatGpuxSbdQbBmFmzXCavgkj7bCy62Zi18dxBcTNcFWwzHI/jRK5efHzyyoBPPr/P46PkrqV786Qa8ZOnmM2PFPD+CxdM9BEv1DuY/uiLyDpZA8CO5S0vrdjZ34vl15o31X0oN40834CV/OTzxiimRbpZXwbvp9CeSzkWXVuz+SmGx8znY0jo9B/OrTedg8js9B+foObiQzhW+s+X0HJyT8xw8+7HxZ1tTC1/C8+t8NG3e9dQw/nQDXtL+7Lxb3q8Pie3aDbfVX2n7LL+zUXRqhhEfWmb7MjaKbr3LunzSShKcRtEN9xhpDlWV/Ch69URjuRyjaNdJDOMDBCsOCFYcEBwhQg9qAmar9p3b6nP0kTyDPU1aPN2IU1O2L2NPk5rusi6/fzkJTk+T1t9tpDmUzfJPk1beayxPzNMklx0dGiA4Qlx2dGiA4OCpcVrhsqNDAwQHDxbcYPe0xeimR70jvrzPly73KI+0Sye/tmiD5lb51o9pb1vSG+404tNTjDj52azNy7Nval+Q1Wt/brc636dtJq3SqsrsNu0MSV/xS7uqypJizDbtPFm8bLx0VZUI33euuTJjJDWM70lzKwt+XQpcNbqZHmnCddG6YNPOK+xDttokOEsFZ6lg0qohmL1MMwnOUsGmPhdW2wjOUsHZ0rrokmJcVjarC85KVheN8H3n2doJpAs+dzrNrQxQcNYuOgu2KNA8BWfNgrP+BGtBCc5KKRjfd559bCYWPNxbjuAyv7Ei1ndwnl/tLpgdogXfwXIdovF95xq07DAWfGYHtzLPDLtF01lSJN0rjZ6DWVG5xpXR64Itv9NzcL54Dja/uhDpOThPz8F867pgElnhOz0H50vPwZY/zl5Oz8H54jk4L41gV/IxwEbRLzxg/P7ws5bVgqPoES1DYq/2EYn8KPrpmZZWyhxFM2QfRTviulUhAYIjxHWrQgIER4jrVoUECFYc9jRp+WTj90cXW1YLPk26rW0j8Zx2iET+adLCWZZWynyaxEjs06Q4AMGKA4IjJ+dYos7WW6JTOpeGCq1olpimgpdMMtKm051FV39BP1TAWm2kghdONeLkRSTc1oxPsh/ROkksFr5bWmOdcC58N9JqZltexm8aLbpDL0ywts6IVXCh3D3dW/xpxq3Ala03RbeyWZs0jTbDYi6tXSDrl0wy0qc/ZalU1QVbWqVls7mFU430yYvIrxe1FhJ1wXZls1zf3cpmSRqtqmSd4DeNls3mXphgKbosEJdgXPqemvb6pIkX8SOHqWtw0fvUNfyXzHpUMOccKpgdIp/uIDiHBZOoC855Cs5hwSTqgnOegjXyfaKmVsTqojUBwSxNihnfcel7qhdPr4MfOaR7cdF7upf/ktlYBKMKBCMRwVo1CMal76lhPL0OfuSQ7sVF7+neki+ZdRFsWu12iLZJQ6ZWeMFktfUQjVwEkzTrIRq5CDa1VuhM5YdokibbIRqXvqeG8fQ6+JHDnqW46H3P0pIvmUU5h8gPsjzSc7bpQ9ofSOzRzpK43WmQ1UZCYRRNX55xGmTZj6JZnwuDLCM4jqL5QdZ6200qjKIlG2RJUfruJJi/TPoJJ5ji8zLJAdHLpP9ab/ty/jKJR/LLpHABwYoDgmPE9Q57QHyl/Q+JB7RTJDo9bPhJKwkVPmxwQPRhw6/X2L6cf9jAkP5hg9s+CQoQHCNu+yQoQHCMuO2ToADBYeJxoWR0M8uVyxWL7LIOMW+K/GpWo8fiKe0Nkt6pfULSaNFddvH9xsum/dbyMlZ0xzpBi+4KlX+08P1rrSlrSmNFd059Zi/niu5KCt9/9ZLtJtKiO43bM7IItsyuY3nYUPBrWyxqXS5YNsteXWhFF0xW64LNZbNYMGnVEFxIp2Wzhd9p2Sz9nZXNZqngQhorm7XvcyHNq2zWEFyyabRsttAJeQTjGf2ts+tMXWNa7SY4a95JfHTYl0yw5ik4axac9RSsmQVnPQXzfXYQnC0pfNcF222iIVjj9owMgvGM/tbZdXy/gz3EqvAOtghO1jsYz+hvnV2nVDB/cuXPNC7RbrHNOZgsD/UcnPV3Dnb88FnxHGzZRHnPwXhGf+vsOnuWmlbnw4CVHI5rIEF0FE1ho+gu7SyJ/HzR3Cia4TGKZig3ivZAZJ/4BgTLg8g+8Q0IlgeRfeIbEKw4rKZ0XDMJok+TKOxp0se0kJGfL/qhZSSwp0kMj6dJjGp7mhQKIFhxQHCkmL961AvNO8VMzn5xtIJpJ0ISzNdkJVMwV//oUSZpV12JUOlcu+z3M9qbltZpVWUhnVZVOhVhsqpK7q+Y+upSAVrSZ1pVybWCjCrq0np5vnVaVclaLRCv4GeuH9mKZ9jZ/+D9zXYZfAWzR6GzU6QvZ7NlF1qzEWxJN+qiHcuo2ScbjPUlq+377thnk2BTM6RM3mW68cJyoy6atV5EAsHnTqdrx/bKI1jzJ5h1gr3Mj+CcgGD7VpMieOSdrXiGnZqxznIEi3wmyfJy4xAt8g4mf904RIu8g1HxZR6CHftsL9ipVQfBsh2i36uZuwvPsPPB1BmbbFNYdzWHz9aV/GofWTX4zFpLa7pgy+/0HFxIp4XvfCdYpOfgQjk9t9q578gc2WL26UL+rzi0yrfOPl3I+ZXkMumtr1YfCrP9NfcakfvwLBtFM/j5orlRNMPnZZIgTl+MxXC4TGLIOIou8n+tb4faPghWHBAsHyL354VZNcGI7GEDhT1sYPDTCXMPGxg+HzYI4vT9wQyHhw2MxD1sKGcfOQKC5aOcfeQICJaPcvaRIyA4TuxvSRvd1LgJX0ujx+qspRmuNb714u95u/TS1mzTy+xzaauWdK9W+c4kQzC5T5N3r4f1WG0uTeVa41s3/54vTbdrzSa9zD7btWpK92qV74wMgvH3yn46aeLFuhH8k5/VX0Rw1hy9t16j0UMwa5VPd1DBp/vps019NPdyS+vOrWpcZ6QQjL9XFk+zUzeCf/Kz+scn2Haneb/X3N6CAn2uTDD/f5sUgvH3yuJpdupG8E9+Vv98wXDp8cor8um0lZLqcBvBNqv5Vs0q7NLL67NTqx6dcVgthWD8vbJ4mp26EfyTn9U/byWb90U46SxNC6UTgq0Kti6BYHf8bS3AA4IVBwQrjvSCgWAAwYoDghUHBCsOCFYcEKw4IFhxQLDigGDFAcGKA4IVBwQrDghWHBCsOCBYcUCw4oBgxQHBigOCFQcEKw4IVhwQrDggWHFAsOKAYMUBwYoDghUHBCsOCFYcEKw4IFhxQLDi/D+J6IbSFWQhkAAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(UpSetR)\n", "library(ggplotify) \n", "gg <- as.ggplot(upset(count.df.bin, nsets = 25, order.by = \"freq\", scale.intersections='log10', scale.set='log10'))\n", "ggsave(\"upsetr.pdf\")\n", "gg" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Saving 6.67 x 6.67 in image\n", "\n" ] }, { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(UpSetR)\n", "gg <- upset(count.df.bin, nsets = 25, order.by = \"freq\", scale.intersections='log10', scale.set='log10')\n", "ggsave(\"upsetr.f.pdf\")\n", "gg" ] }, { "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": "3.6.15" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }