{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "#append UMIs\n", "nohup sh -c \"ls *_R1_001.fastq.gz | parallel -j12 -k python append_umi.py {}\" &" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "shopt -s extglob\n", "for f in *.fastq.gz; do\n", "out=${f/.fastq.gz/.trim.fastq.gz}\n", "echo \"cutadapt -j 12 -m 15 -u 3 -a AAAAAAAAAAAAAAAA -o $out $f;\"\n", "cutadapt -j 12 -m 15 -u 3 -a AAAAAAAAAAAAAAAA -o $out $f;\n", "done" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.trim.fastq.gz; do\n", "out=${f/.trim.fastq.gz/.bam}\n", "echo \"bowtie2 --sensitive --end-to-end -N 1 -p 12 -x /avicenna/genomes/hg38/hg38 -U $f | samtools view - -Sb -h -t /avicenna/genomes/hg38/hg38.fa.fai -o $out\";\n", "bowtie2 --sensitive --end-to-end -N 1 -p 12 -x /avicenna/genomes/hg38/hg38 -U $f | samtools view - -Sb -h -t /avicenna/genomes/hg38/hg38.fa.fai -o $out ;\n", "done" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.bam; do\n", "out=${f/.bam/.srt.bam};\n", "echo \"samtools sort -o $out $f\";\n", "samtools sort -@ 12 -m 2G -o $out $f\n", "samtools index $out;\n", "done" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.srt.bam; do\n", "out=${f/.srt.bam/.srt.dd.bam};\n", "echo \"umi_tools dedup -I $f -S $out\";\n", "umi_tools dedup -I $f -S $out &\n", "done" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.srt.dd.bam; do\n", "samtools index $f &\n", "done" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.dd.bam; do\n", "out=${f/.dd.bam/.dd.bed}\n", "bamToBed -i $f > $out\n", "done" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "cat *.dd.bed | sort -k1,1 -k2,2n | \\\n", "mergeBed -s -c 4,6 -o count,distinct -i stdin | \\\n", "awk '{print $1 \"\\t\" $2 \"\\t\" $3 \"\\t\" $1\":\"$2\"-\"$3\":\"$5 \"\\t\" $4 \"\\t\" $5}' > combined_smRNA_loci.bed" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "#remove peaks with only one read\n", "awk '{if($5>1) print}' combined_smRNA_loci.bed > combined_smRNA_loci.c.bed" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.dd.bam; do\n", "out=${f/.dd.bam/.tRNA.cnt}\n", "echo \"samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_tRNA.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \\\"\\t\\\" $1}' > $out\"\n", "samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_tRNA.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \"\\t\" $1}' > $out &\n", "done" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.dd.bam; do\n", "out=${f/.dd.bam/.smRNA.cnt}\n", "echo \"samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b combined_smRNA_loci.c.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \\\"\\t\\\" $1}' > $out\"\n", "samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b combined_smRNA_loci.c.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \"\\t\" $1}' > $out &\n", "done" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "samtools view -q 10 -b A2508.srt.dd.bam > A2508.srt.dd.q10.bam\n", "samtools view -q 10 -b A375.srt.dd.bam > A375.srt.dd.q10.bam\n", "samtools view -q 10 -b A549.srt.dd.bam > A549.srt.dd.q10.bam\n", "samtools view -q 10 -b BxPC3.srt.dd.bam > BxPC3.srt.dd.q10.bam\n", "samtools view -q 10 -b C42Bu.srt.dd.bam > C42Bu.srt.dd.q10.bam\n", "samtools view -q 10 -b Colo320.srt.dd.bam > Colo320.srt.dd.q10.bam\n", "samtools view -q 10 -b H1299.srt.dd.bam > H1299.srt.dd.q10.bam\n", "samtools view -q 10 -b H23u.srt.dd.bam > H23u.srt.dd.q10.bam\n", "samtools view -q 10 -b H358u.srt.dd.bam > H358u.srt.dd.q10.bam\n", "samtools view -q 10 -b HCC44.srt.dd.bam > HCC44.srt.dd.q10.bam\n", "samtools view -q 10 -b HCT116.srt.dd.bam > HCT116.srt.dd.q10.bam\n", "samtools view -q 10 -b HEK293T-16.srt.dd.bam > HEK293T-16.srt.dd.q10.bam\n", "samtools view -q 10 -b HEK293T-1.srt.dd.bam > HEK293T-1.srt.dd.q10.bam\n", "samtools view -q 10 -b HEK293T-26.srt.dd.bam > HEK293T-26.srt.dd.q10.bam\n", "samtools view -q 10 -b HEK293T-9.srt.dd.bam > HEK293T-9.srt.dd.q10.bam\n", "samtools view -q 10 -b HEPG2.srt.dd.bam > HEPG2.srt.dd.q10.bam\n", "samtools view -q 10 -b HS707A.srt.dd.bam > HS707A.srt.dd.q10.bam\n", "samtools view -q 10 -b K562.srt.dd.bam > K562.srt.dd.q10.bam\n", "samtools view -q 10 -b LNCaP.srt.dd.bam > LNCaP.srt.dd.q10.bam\n", "samtools view -q 10 -b LS174T.srt.dd.bam > LS174T.srt.dd.q10.bam\n", "samtools view -q 10 -b MCF7.srt.dd.bam > MCF7.srt.dd.q10.bam\n", "samtools view -q 10 -b MDA231.srt.dd.bam > MDA231.srt.dd.q10.bam\n", "samtools view -q 10 -b MDA453.srt.dd.bam > MDA453.srt.dd.q10.bam\n", "samtools view -q 10 -b PACA2.srt.dd.bam > PACA2.srt.dd.q10.bam\n", "samtools view -q 10 -b PANC1.srt.dd.bam > PANC1.srt.dd.q10.bam\n", "samtools view -q 10 -b PC3.srt.dd.bam > PC3.srt.dd.q10.bam\n", "samtools view -q 10 -b SW480u.srt.dd.bam > SW480u.srt.dd.q10.bam\n", "samtools view -q 10 -b SW620u.srt.dd.bam > SW620u.srt.dd.q10.bam\n", "samtools view -q 10 -b ZR751.srt.dd.bam > ZR751.srt.dd.q10.bam\n" ] } ], "source": [ "%%bash\n", "#uniquely mapped reads\n", "for f in *.dd.bam; do\n", "out=${f/.dd.bam/.dd.q10.bam};\n", "echo \"samtools view -q 10 -b $f > $out\"\n", "samtools view -q 10 -b $f > $out\n", "done" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.dd.q10.bam; do\n", "echo $f >> lib_size.q10.txt\n", "samtools view $f | wc -l >> lib_size.q10.txt\n", "done" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "for f in *.dd.bam; do\n", "out=${f/.dd.bam/.smRNA.cnt}\n", "echo \"samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b combined_smRNA_loci.c.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \\\"\\t\\\" $1}' > $out\"\n", "samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b combined_smRNA_loci.c.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \"\\t\" $1}' > $out &\n", "done" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "samtools view -h A2508.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > A2508.srt.annotated.cnt\n", "samtools view -h A375.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > A375.srt.annotated.cnt\n", "samtools view -h A549.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > A549.srt.annotated.cnt\n", "samtools view -h BxPC3.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > BxPC3.srt.annotated.cnt\n", "samtools view -h C42Bu.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > C42Bu.srt.annotated.cnt\n", "samtools view -h Colo320.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > Colo320.srt.annotated.cnt\n", "samtools view -h H1299.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > H1299.srt.annotated.cnt\n", "samtools view -h H23u.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > H23u.srt.annotated.cnt\n", "samtools view -h H358u.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > H358u.srt.annotated.cnt\n", "samtools view -h HCC44.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HCC44.srt.annotated.cnt\n", "samtools view -h HCT116.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HCT116.srt.annotated.cnt\n", "samtools view -h HEK293T-16.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HEK293T-16.srt.annotated.cnt\n", "samtools view -h HEK293T-1.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HEK293T-1.srt.annotated.cnt\n", "samtools view -h HEK293T-26.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HEK293T-26.srt.annotated.cnt\n", "samtools view -h HEK293T-9.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HEK293T-9.srt.annotated.cnt\n", "samtools view -h HEPG2.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HEPG2.srt.annotated.cnt\n", "samtools view -h HS707A.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > HS707A.srt.annotated.cnt\n", "samtools view -h K562.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > K562.srt.annotated.cnt\n", "samtools view -h LNCaP.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > LNCaP.srt.annotated.cnt\n", "samtools view -h LS174T.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > LS174T.srt.annotated.cnt\n", "samtools view -h MCF7.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > MCF7.srt.annotated.cnt\n", "samtools view -h MDA231.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > MDA231.srt.annotated.cnt\n", "samtools view -h MDA453.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > MDA453.srt.annotated.cnt\n", "samtools view -h PACA2.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > PACA2.srt.annotated.cnt\n", "samtools view -h PANC1.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > PANC1.srt.annotated.cnt\n", "samtools view -h PC3.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > PC3.srt.annotated.cnt\n", "samtools view -h SW480u.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > SW480u.srt.annotated.cnt\n", "samtools view -h SW620u.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > SW620u.srt.annotated.cnt\n", "samtools view -h ZR751.srt.dd.bam | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print \"\\t\" }' > ZR751.srt.annotated.cnt\n" ] } ], "source": [ "%%bash\n", "for f in *.dd.bam; do\n", "out=${f/.dd.bam/.annotated.cnt}\n", "echo \"samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \\\"\\t\\\" $1}' > $out\"\n", "samtools view -h $f | bamToBed -i stdin | intersectBed -s -wo -a stdin -b /avicenna/genomes/hg38/hg38_smRNAs.bed | cut -f10 | sort | uniq -c | awk '{ print $2 \"\\t\" $1}' > $out &\n", "done" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rpy2.ipython extension is already loaded. To reload it, use:\n", " %reload_ext rpy2.ipython\n" ] } ], "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": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " A2508 A375 A549 BxPC3 C42Bu Colo320 H1299 H23u H358u\n", "nm-tRNA-SerTGA-AGA-chr11-7 3 1 1 1 0 0 0 0 1\n", "nm-tRNA-Tyr-ATA-chr11-8 1 0 0 0 1 0 0 0 0\n", "nm-tRNA-Tyr-GTA-chr1-142 875 794 461 506 188 507 292 522 539\n", "nm-tRNA-Tyr-GTA-chr14-8 867 766 407 462 192 528 281 504 557\n", "nm-tRNA-Tyr-GTA-chr2-12 1 0 0 0 1 3 0 0 2\n", "nm-tRNA-Tyr-GTA-chr21-2 866 767 441 469 169 503 292 573 574\n", " HCC44 HCT116 HEK293T.1 HEK293T.16 HEK293T.26\n", "nm-tRNA-SerTGA-AGA-chr11-7 0 0 2 1 2\n", "nm-tRNA-Tyr-ATA-chr11-8 0 0 1 1 0\n", "nm-tRNA-Tyr-GTA-chr1-142 508 470 308 338 519\n", "nm-tRNA-Tyr-GTA-chr14-8 489 453 344 355 454\n", "nm-tRNA-Tyr-GTA-chr2-12 0 0 1 1 0\n", "nm-tRNA-Tyr-GTA-chr21-2 527 502 340 362 502\n", " HEK293T.9 HEPG2 HS707A K562 LNCaP LS174T MCF7 MDA231\n", "nm-tRNA-SerTGA-AGA-chr11-7 1 3 0 3 1 3 4 1\n", "nm-tRNA-Tyr-ATA-chr11-8 0 0 0 1 2 0 0 0\n", "nm-tRNA-Tyr-GTA-chr1-142 423 400 387 684 337 404 832 377\n", "nm-tRNA-Tyr-GTA-chr14-8 438 358 384 730 322 405 863 373\n", "nm-tRNA-Tyr-GTA-chr2-12 0 1 0 0 1 0 0 0\n", "nm-tRNA-Tyr-GTA-chr21-2 439 395 339 716 293 349 868 393\n", " MDA453 PACA2 PANC1 PC3 SW480u SW620u ZR751\n", "nm-tRNA-SerTGA-AGA-chr11-7 6 0 1 0 1 1 0\n", "nm-tRNA-Tyr-ATA-chr11-8 0 0 0 0 0 0 0\n", "nm-tRNA-Tyr-GTA-chr1-142 458 529 239 295 447 613 315\n", "nm-tRNA-Tyr-GTA-chr14-8 483 564 242 337 457 655 319\n", "nm-tRNA-Tyr-GTA-chr2-12 0 0 0 0 1 0 0\n", "nm-tRNA-Tyr-GTA-chr21-2 466 536 236 305 466 635 335\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "files<-list.files(pattern=\".tRNA.cnt\")\n", "datalist = lapply(files, function(x){read.table(file=x,header=FALSE,col.names=c(\"tRF\", sub(\".srt.tRNA.cnt\", \"\", x)))})\n", "m <- Reduce(function(...) merge(..., by=1, all = TRUE), datalist)\n", "rownames(m) <- m[,1]\n", "m[is.na(m)] <- 0\n", "m <- m[,-1]\n", "head(m)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " A2508 A375 A549 BxPC3 C42Bu Colo320 H1299\n", "chr1_KI270706v1_random:16702-16720:+ 1 4 2 0 4 6 5\n", " H23u H358u HCC44 HCT116 HEK293T.1\n", "chr1_KI270706v1_random:16702-16720:+ 5 3 5 0 3\n", " HEK293T.16 HEK293T.26 HEK293T.9 HEPG2\n", "chr1_KI270706v1_random:16702-16720:+ 4 0 2 4\n", " HS707A K562 LNCaP LS174T MCF7 MDA231\n", "chr1_KI270706v1_random:16702-16720:+ 1 3 6 0 1 2\n", " MDA453 PACA2 PANC1 PC3 SW480u SW620u ZR751\n", "chr1_KI270706v1_random:16702-16720:+ 3 0 2 3 4 4 5\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "files<-list.files(pattern=\".smRNA.cnt\")\n", "datalist = lapply(files, function(x){read.table(file=x,header=FALSE,col.names=c(\"smRNA\", sub(\".srt.smRNA.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": 76, "metadata": {}, "outputs": [], "source": [ "%%R\n", "library(DESeq2)\n", "colData <- data.frame(cond=colnames(tmp), row.names=colnames(tmp))\n", "dds <- DESeqDataSetFromMatrix(countData = tmp, colData=colData, design = ~ cond)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " A2508 A375 A549 BxPC3 C42Bu Colo320 H1299 \n", " 0.8256935 0.9923271 1.1037352 1.0105470 1.0176283 1.4778089 0.9678724 \n", " H23u H358u HCC44 HCT116 HEK293T.1 HEK293T.16 HEK293T.26 \n", " 1.0353598 0.9567173 1.4534567 0.9563961 0.8791460 0.9661940 1.1574294 \n", " HEK293T.9 HEPG2 HS707A K562 LNCaP LS174T MCF7 \n", " 1.1136977 1.0220953 0.9445823 1.2421130 1.2262184 0.8602644 1.0403348 \n", " MDA231 MDA453 PACA2 PANC1 PC3 SW480u SW620u \n", " 1.1131138 1.0942638 1.1119712 0.7794847 0.7744898 1.1932962 1.0305997 \n", " ZR751 \n", " 0.8341806 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "dds <- estimateSizeFactors(dds)\n", "sf <- sizeFactors(dds)\n", "ncu <- counts(dds, normalized=TRUE)\n", "write.table(tmp, file=\"smRNA_counts.txt\", sep=\"\\t\", quote=FALSE, col.names=NA )\n", "write.table(ncu, file=\"smRNA_norm-counts.txt\", sep=\"\\t\", quote=FALSE, col.names=NA )\n", "sf" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " A2508 A375 A549 BxPC3\n", "nm-tRNA-SerTGA-AGA-chr11-7 3.633309 1.007732 0.9060144 0.9895631\n", "nm-tRNA-Tyr-ATA-chr11-8 1.211103 0.000000 0.0000000 0.0000000\n", "nm-tRNA-Tyr-GTA-chr1-142 1059.715236 800.139362 417.6726390 500.7189382\n", "nm-tRNA-Tyr-GTA-chr14-8 1050.026411 771.922860 368.7478614 457.1781610\n", "nm-tRNA-Tyr-GTA-chr2-12 1.211103 0.000000 0.0000000 0.0000000\n", "nm-tRNA-Tyr-GTA-chr21-2 1048.815308 772.930593 399.5523510 464.1051028\n", " C42Bu Colo320 H1299 H23u H358u\n", "nm-tRNA-SerTGA-AGA-chr11-7 0.000000 0.000000 0.0000 0.0000 1.045241\n", "nm-tRNA-Tyr-ATA-chr11-8 0.982677 0.000000 0.0000 0.0000 0.000000\n", "nm-tRNA-Tyr-GTA-chr1-142 184.743282 343.075491 301.6927 504.1726 563.384822\n", "nm-tRNA-Tyr-GTA-chr14-8 188.673990 357.285718 290.3275 486.7873 582.199158\n", "nm-tRNA-Tyr-GTA-chr2-12 0.982677 2.030032 0.0000 0.0000 2.090482\n", "nm-tRNA-Tyr-GTA-chr21-2 166.072418 340.368781 301.6927 553.4308 599.968253\n", " HCC44 HCT116 HEK293T.1 HEK293T.16 HEK293T.26\n", "nm-tRNA-SerTGA-AGA-chr11-7 0.0000 0.0000 2.274935 1.034989 1.727967\n", "nm-tRNA-Tyr-ATA-chr11-8 0.0000 0.0000 1.137467 1.034989 0.000000\n", "nm-tRNA-Tyr-GTA-chr1-142 349.5116 491.4282 350.339986 349.826220 448.407495\n", "nm-tRNA-Tyr-GTA-chr14-8 336.4393 473.6531 391.288816 367.421030 392.248560\n", "nm-tRNA-Tyr-GTA-chr2-12 0.0000 0.0000 1.137467 1.034989 0.000000\n", "nm-tRNA-Tyr-GTA-chr21-2 362.5839 524.8871 386.738946 374.665951 433.719773\n", " HEK293T.9 HEPG2 HS707A K562\n", "nm-tRNA-SerTGA-AGA-chr11-7 0.8979098 2.9351469 0.0000 2.4152391\n", "nm-tRNA-Tyr-ATA-chr11-8 0.0000000 0.0000000 0.0000 0.8050797\n", "nm-tRNA-Tyr-GTA-chr1-142 379.8158322 391.3529244 409.7049 550.6745203\n", "nm-tRNA-Tyr-GTA-chr14-8 393.2844788 350.2608673 406.5289 587.7081869\n", "nm-tRNA-Tyr-GTA-chr2-12 0.0000000 0.9783823 0.0000 0.0000000\n", "nm-tRNA-Tyr-GTA-chr21-2 394.1823885 386.4610128 358.8888 576.4370709\n", " LNCaP LS174T MCF7 MDA231\n", "nm-tRNA-SerTGA-AGA-chr11-7 0.8155154 3.4873 3.844916 0.8983807\n", "nm-tRNA-Tyr-ATA-chr11-8 1.6310308 0.0000 0.000000 0.0000000\n", "nm-tRNA-Tyr-GTA-chr1-142 274.8286858 469.6230 799.742572 338.6895331\n", "nm-tRNA-Tyr-GTA-chr14-8 262.5959549 470.7855 829.540673 335.0960102\n", "nm-tRNA-Tyr-GTA-chr2-12 0.8155154 0.0000 0.000000 0.0000000\n", "nm-tRNA-Tyr-GTA-chr21-2 238.9460087 405.6892 834.346818 353.0636247\n", " MDA453 PACA2 PANC1 PC3 SW480u\n", "nm-tRNA-SerTGA-AGA-chr11-7 5.483139 0.0000 1.282899 0.0000 0.8380149\n", "nm-tRNA-Tyr-ATA-chr11-8 0.000000 0.0000 0.000000 0.0000 0.0000000\n", "nm-tRNA-Tyr-GTA-chr1-142 418.546244 475.7318 306.612814 380.8959 374.5926481\n", "nm-tRNA-Tyr-GTA-chr14-8 441.392655 507.2074 310.461510 435.1252 382.9727968\n", "nm-tRNA-Tyr-GTA-chr2-12 0.000000 0.0000 0.000000 0.0000 0.8380149\n", "nm-tRNA-Tyr-GTA-chr21-2 425.857096 482.0269 302.764117 393.8077 390.5149306\n", " SW620u ZR751\n", "nm-tRNA-SerTGA-AGA-chr11-7 0.9703089 0.0000\n", "nm-tRNA-Tyr-ATA-chr11-8 0.0000000 0.0000\n", "nm-tRNA-Tyr-GTA-chr1-142 594.7993350 377.6161\n", "nm-tRNA-Tyr-GTA-chr14-8 635.5523074 382.4112\n", "nm-tRNA-Tyr-GTA-chr2-12 0.0000000 0.0000\n", "nm-tRNA-Tyr-GTA-chr21-2 616.1461301 401.5917\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(DESeq2)\n", "colData <- data.frame(cond=colnames(m), row.names=colnames(m))\n", "dds <- DESeqDataSetFromMatrix(countData = m, colData=colData, design = ~ cond)\n", "sizeFactors(dds) <- sf\n", "ncu <- counts(dds, normalized=TRUE)\n", "head(ncu)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "%%R\n", "write.table(m, file=\"tRF_counts.txt\", sep=\"\\t\", quote=FALSE, col.names=NA )\n", "write.table(ncu, file=\"tRF_norm-counts.txt\", sep=\"\\t\", quote=FALSE, col.names=NA )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Annotated smRNAs" ] }, { "cell_type": "code", "execution_count": 1, "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": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A2508 A375 A549 BxPC3 C42Bu Colo320 H1299 H23u H358u HCC44 HCT116\n", "14q(0) 1 0 71 810 0 1 0 105 0 1 1\n", " HEK293T.1 HEK293T.16 HEK293T.26 HEK293T.9 HEPG2 HS707A K562 LNCaP LS174T\n", "14q(0) 1 0 1 1 0 484 3 0 0\n", " MCF7 MDA231 MDA453 PACA2 PANC1 PC3 SW480u SW620u ZR751\n", "14q(0) 6 0 1 4 249 2 1 1 0\n" ] } ], "source": [ "%%R\n", "files<-list.files(pattern=\".annotated.cnt\")\n", "datalist = lapply(files, function(x){read.table(file=x,header=FALSE,col.names=c(\"smRNA\", sub(\".srt.annotated.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": 3, "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": 4, "metadata": {}, "outputs": [], "source": [ "%%R\n", "rownames(meta) <- gsub(\"-\", \".\", rownames(meta))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 1506 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": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Loading required package: lattice\n", "\n", "R[write to console]: Loading required package: survival\n", "\n", "R[write to console]: Loading required package: Formula\n", "\n", "R[write to console]: Loading required package: ggplot2\n", "\n", "R[write to console]: \n", "Attaching package: ‘Hmisc’\n", "\n", "\n", "R[write to console]: The following objects are masked from ‘package:base’:\n", "\n", " format.pval, units\n", "\n", "\n" ] }, { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(Hmisc)\n", "rc <- rcorr(as.matrix(as.data.frame(lapply(count.df, as.numeric))))\n", "heatmap(rc$r)" ] }, { "cell_type": "code", "execution_count": 7, "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]: \n", "Attaching package: ‘Biobase’\n", "\n", "\n", "R[write to console]: The following object is masked from ‘package:Hmisc’:\n", "\n", " contents\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": 8, "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\"))\n" ] }, { "cell_type": "code", "execution_count": 9, "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(\"annotated_smRNA_PCA.pdf\", width=4, height=3)\n", "g" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "%%R\n", "write.table(count.df, file=\"annotated_smRNA_counts.txt\", sep=\"\\t\", quote=FALSE, col.names=NA )\n", "write.table(counts(dds, normalized=TRUE), file=\"annotated_smRNA_norm-counts.txt\", sep=\"\\t\", quote=FALSE, col.names=NA )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A2058 A375 A549 BxPC3 C42B Colo320 H1299\n", "14q(0) 1.19952 0.000000 65.114776 725.90207 0 0.7687066 0.000000\n", "14q(I-3) 2.39904 0.000000 24.761957 244.65588 0 0.0000000 0.000000\n", "14q(I-7) 1.19952 1.022007 57.777900 577.13695 0 0.7687066 3.106253\n", "14q(I-8) 1.19952 0.000000 34.850162 440.91829 0 0.0000000 4.141670\n", "14q(II-12) 1.19952 2.044014 47.689695 523.36643 0 0.0000000 2.070835\n", "14q(II-21) 1.19952 0.000000 1.834219 34.95084 0 0.0000000 0.000000\n", " H23 H358 HCC44 HCT116 HEPG2 HS707A K562\n", "14q(0) 93.592965 0 0.6846345 0.9900088 0.000000 568.57939 2.4756791\n", "14q(I-3) 54.373056 0 0.0000000 0.0000000 0.000000 269.01794 0.0000000\n", "14q(I-7) 74.874372 0 0.0000000 0.9900088 1.166626 673.13222 0.8252264\n", "14q(I-8) 70.417564 0 0.0000000 2.9700265 0.000000 684.87973 0.8252264\n", "14q(II-12) 83.787988 0 0.6846345 0.9900088 0.000000 683.70497 1.6504527\n", "14q(II-21) 6.239531 0 0.0000000 0.0000000 0.000000 39.94153 0.0000000\n", " LNCaP LS174T MCF7 MDA-MB-231 MDA-453 MIAPaCa2 PANC1\n", "14q(0) 0.0000000 0 5.7647588 0.000000 0.9391119 3.7134736 280.73609\n", "14q(I-3) 0.0000000 0 0.9607931 0.000000 0.0000000 0.9283684 96.96106\n", "14q(I-7) 0.0000000 0 0.9607931 0.000000 0.0000000 0.0000000 285.24590\n", "14q(I-8) 0.0000000 0 1.9215863 1.084315 0.9391119 0.0000000 214.21629\n", "14q(II-12) 0.7961387 0 5.7647588 0.000000 1.8782239 0.0000000 240.14774\n", "14q(II-21) 0.0000000 0 0.0000000 0.000000 0.0000000 0.0000000 21.42163\n", " PC3 SW480 SW620 ZR-75-1\n", "14q(0) 2.525613 0.886856 0.9155641 0.000000\n", "14q(I-3) 1.262807 0.000000 0.0000000 1.111609\n", "14q(I-7) 6.314034 1.773712 0.9155641 0.000000\n", "14q(I-8) 6.314034 0.886856 0.0000000 0.000000\n", "14q(II-12) 7.576840 0.886856 0.0000000 3.334828\n", "14q(II-21) 0.000000 0.000000 0.0000000 0.000000\n" ] } ], "source": [ "%%R\n", "count.df.norm <- counts(dds, normalized=TRUE)\n", "head(count.df.norm)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 1506 25\n" ] } ], "source": [ "%%R \n", "count.df.z <- t(apply(count.df.norm, 1, scale))\n", "colnames(count.df.z) <- colnames(count.df.norm)\n", "dim(count.df.z)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 174 25\n" ] } ], "source": [ "%%R\n", "c.v <- apply(count.df.norm, 1, sd) / rowSums(count.df.norm)\n", "count.df.z.f <- count.df.z[c.v>0.15,]\n", "dim(count.df.z.f)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "hist(c.v)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0% 25% 50% 75% 100% \n", "-0.2657161 -0.2000000 -0.2000000 -0.2000000 4.8000000 \n" ] } ], "source": [ "%%R\n", "quantile(count.df.z.f, probs = seq(0, 1, 1/4), na.rm=T)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "%%R\n", "count.df.z.f[is.na(count.df.z.f)] <- 0" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(tidyverse)\n", "library(pheatmap)\n", "library(RColorBrewer)\n", "library(viridis)\n", "breaksList = seq(-2, 2, by = 0.05)\n", "plt <- pheatmap(count.df.z.f,\n", " color=inferno(length(breaksList)-1),\n", " breaks = breaksList,\n", " annotation_col=colData,\n", " show_rownames=T,\n", " treeheight_row=0,\n", " treeheight_col=20,\n", " border_color=NA)\n", "pdf('annotated_smRNA_heatmap.pdf', width=7.5, height=11)\n", "grid::grid.newpage()\n", "grid::grid.draw(plt$gtable)\n", "dev.off()\n", "plt" ] }, { "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" } }, "nbformat": 4, "nbformat_minor": 2 }