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15_DEG_DESeq2_analysis.Rmd
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15_DEG_DESeq2_analysis.Rmd
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---
title: "Differential Gene Expression for cancer types using DEseq2"
author: "Sonali Arora, Hamid Bolouri"
date: "November 18, 2019"
output:
html_document:
toc: true
theme: united
---
In this vignette, we compare three types of cancer types using DESeq2 for each of the pipelines.
DESeq2 requires raw counts to perform differential gene expression analysis.
Each pipeline in addition to RPKM data, has also made raw counts available, thus, we obtained the raw counts from each pipeline.
They can be downloaded from our amazon s3 bucked using :
```{}
wget --recursive -nH --cut-dirs=1 https://s3-us-west-2.amazonaws.com/fh-pi-holland-e/OriginalTCGAGTExData/raw_counts/index.html
```
## Breast comparison
In the next few code chunks, we find differentially expressed genes (DEGs) between Basal like sub type and
HER2E (HER2 enriched) sub type using DESeq2 from each of the pipelines. A statistical cutoff of
FDR < 0.05 & abs(log2fold change) >1 was used to find DEGs.
### GDC
```{r eval = FALSE}
rm(list=ls())
library(SummarizedExperiment)
library(DESeq2)
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "breast")
brca = get(load(file.path(s3_dir, "gdc_TCGA-BRCA_counts_10_28_2019.Rdata")))
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
basal_like_samples= read.delim(file.path(s3_dir,"brca_basal_like_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
HER2_enriched_samples= read.delim(file.path(s3_dir,"brca_HER2_enriched_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
brca = brca[ match( goi, rowRanges(brca)$external_gene_name), ]
a_idx =match( basal_like_samples, colData(brca)[,2] )
b_idx =match( HER2_enriched_samples, colData(brca)[,2] )
basal_like = assay(brca[, a_idx])
HER2_enriched = assay(brca[, b_idx])
rawdata = cbind(basal_like, HER2_enriched)
coldata = cbind( sampleName = c(colnames(basal_like), colnames(HER2_enriched)),
sampleGroup = c(rep("basal_like", ncol(basal_like)),
rep("HER2_enriched", ncol(HER2_enriched)) ))
map = cbind(rownames(rawdata), goi)
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc,
contrast = c("sampleGroup","basal_like", "HER2_enriched") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = map[match(rownames(resdf), map[,1]), 2],
geneID = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "HER2_enriched_vs_basal_like_gdc_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "HER2_enriched_vs_basal_like_gdc_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "HER2_enriched_vs_basal_like_gdc_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### MSKCC
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "breast")
brca = read.delim(file.path(s3_dir, "brca-rsem-count-tcga-t.txt"),
header=T, stringsAsFactors = FALSE, row.names = 1, check.names = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"),
header=FALSE, stringsAsFactors = FALSE )[,1]
basal_like_samples= read.delim(file.path(s3_dir,"brca_basal_like_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
HER2_enriched_samples= read.delim(file.path(s3_dir,"brca_HER2_enriched_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
basal_like = brca[ match( goi, rownames(brca)), ]
HER2_enriched = brca[ match( goi, rownames(brca)), ]
basal_like = brca[, match( basal_like_samples, substr(colnames(brca), 1,12)) ]
HER2_enriched = brca[ ,match( HER2_enriched_samples, substr(colnames(brca), 1,12)) ]
library(DESeq2)
rawdata = cbind(basal_like, HER2_enriched)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(basal_like), colnames(HER2_enriched)),
sampleGroup = c(rep("basal_like", ncol(basal_like)),
rep("HER2_enriched", ncol(HER2_enriched)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc,
contrast = c("sampleGroup","basal_like", "HER2_enriched") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName =rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "HER2_enriched_vs_basal_like_mskcc_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "HER2_enriched_vs_basal_like_mskcc_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "HER2_enriched_vs_basal_like_mskcc_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Recount2
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
recount_dir = file.path(bigdir, "OriginalTCGAGTExData", "datasource_RECOUNT2_TCGA")
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "breast")
library(SummarizedExperiment)
rse_gene = get(load(file.path(recount_dir, "rse_gene.Rdata")))
col_nms = c("gdc_cases.samples.portions.analytes.aliquots.submitter_id",
"cgc_case_batch_number",
"gdc_cases.project.project_id", "auc")
col_idx = match(col_nms, colnames(colData(rse_gene)))
colData(rse_gene) = colData(rse_gene)[, col_idx]
goi= read.delim(file.path(s3_dir, "de_goi.txt"),
header=FALSE, stringsAsFactors = FALSE )[,1]
basal_like_samples= read.delim(file.path(s3_dir,"brca_basal_like_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
HER2_enriched_samples= read.delim(file.path(s3_dir,"brca_HER2_enriched_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
test = sapply(rowRanges(rse_gene)$symbol, function(x) x[1])
row_idx = match(goi, test)
basal_like = rse_gene[, match( basal_like_samples, substr(colData(rse_gene)[,1], 1, 12))]
HER2_enriched = rse_gene[, match( HER2_enriched_samples, substr(colData(rse_gene)[,1], 1, 12))]
basal_like = basal_like[ row_idx, ]
HER2_enriched = HER2_enriched[ row_idx, ]
library("recount")
basal_like <- scale_counts(basal_like, round = TRUE)
HER2_enriched <- scale_counts(HER2_enriched, round = TRUE)
basal_like = assay(basal_like)
HER2_enriched = assay(HER2_enriched)
rownames(basal_like) = goi
rownames(HER2_enriched) = goi
colnames(basal_like) = basal_like_samples
colnames(HER2_enriched) = HER2_enriched_samples
library(DESeq2)
rawdata = cbind(basal_like, HER2_enriched)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(basal_like), colnames(HER2_enriched)),
sampleGroup = c(rep("basal_like", ncol(basal_like)),
rep("HER2_enriched", ncol(HER2_enriched)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc,
contrast = c("sampleGroup", "basal_like", "HER2_enriched") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "HER2_enriched_vs_basal_like_RECOUNT2_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "HER2_enriched_vs_basal_like_RECOUNT2_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "HER2_enriched_vs_basal_like_RECOUNT2_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Xena/Toil
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "breast")
data = read.delim(file.path(s3_dir,"OnlyTcga_gene_expected_count"),
header = T, stringsAsFactors = FALSE, row.names = 1,
check.names = FALSE)
map = read.delim(file.path(s3_dir, "gene_map_xena.txt"), header=T, stringsAsFactors = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"),
header=FALSE, stringsAsFactors = FALSE )[,1]
basal_like_samples= read.delim(file.path(s3_dir,"brca_basal_like_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
HER2_enriched_samples= read.delim(file.path(s3_dir,"brca_HER2_enriched_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
basal_like = data[, match( basal_like_samples, substr(colnames(data), 1,12)) ]
HER2_enriched = data[ ,match( HER2_enriched_samples, substr(colnames(data), 1,12)) ]
row_idx = map[match(goi, map[,2]), 1]
basal_like = basal_like[ row_idx, ]
HER2_enriched = HER2_enriched[ row_idx, ]
basal_like =2^basal_like - 1
HER2_enriched =2^HER2_enriched - 1
library(DESeq2)
rawdata = cbind(basal_like, HER2_enriched)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(basal_like), colnames(HER2_enriched)),
sampleGroup = c(rep("basal_like", ncol(basal_like)), rep("HER2_enriched", ncol(HER2_enriched)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc,
contrast = c("sampleGroup", "basal_like", "HER2_enriched") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = map[match(rownames(resdf), map[,1]), 2],
geneID =rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "HER2_enriched_vs_basal_like_XENA_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "HER2_enriched_vs_basal_like_XENA_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "HER2_enriched_vs_basal_like_XENA_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Piccolo
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "breast")
data = read.delim(file.path(s3_dir, "GSM1536837_01_27_15_TCGA_20.Illumina.tumor_Rsubread_FeatureCounts.txt"),
header = T, stringsAsFactors = FALSE, row.names = 1, check.names = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"),
header=FALSE, stringsAsFactors = FALSE )[,1]
basal_like_samples= read.delim(file.path(s3_dir,"brca_basal_like_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
HER2_enriched_samples= read.delim(file.path(s3_dir,"brca_HER2_enriched_samples.txt"),
header=TRUE, stringsAsFactors = FALSE )[,1]
basal_like = data[, match(basal_like_samples, substr(colnames(data), 1 ,12) )]
HER2_enriched = data[, match(HER2_enriched_samples, substr(colnames(data), 1 ,12) )]
row_idx =match( goi, rownames(data))
basal_like = basal_like[ row_idx, ]
HER2_enriched = HER2_enriched[ row_idx, ]
library(DESeq2)
rawdata = cbind(basal_like, HER2_enriched)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(basal_like), colnames(HER2_enriched)),
sampleGroup = c(rep("basal_like", ncol(basal_like)),
rep("HER2_enriched", ncol(HER2_enriched)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc,
contrast = c("sampleGroup","basal_like", "HER2_enriched" ) )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "HER2_enriched_vs_basal_like_Piccolo_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "HER2_enriched_vs_basal_like_Piccolo_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "HER2_enriched_vs_basal_like_Piccolo_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
## Lung Comparison
In the next few code chunks , we find DEGs between Lung adenocarcinoma(TCGA-LUAD)
compared to Lung squamous cell carcinoma ( TCGA-LUSC) using DESeq2 from each of the pipelines. A statistical cutoff of
FDR < 0.05 & abs(log2foldchange) >1 was used to find DEGs.
### GDC
```{r eval = FALSE}
rm(list=ls())
library(SummarizedExperiment)
library(DESeq2)
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "lung")
luad = get(load(file.path(s3_dir, "gdc_TCGA-LUAD_counts_10_28_2019.Rdata")))
lusc= get(load(file.path(s3_dir, "gdc_TCGA-LUSC_counts_10_28_2019.Rdata")))
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad_samples= read.delim(file.path(s3_dir,"luad_samples.txt"),
header=FALSE, stringsAsFactors = FALSE )[,1]
lusc_samples= read.delim(file.path(s3_dir,"lusc_samples.txt"),
header=FALSE, stringsAsFactors = FALSE )[,1]
luad = luad[ ,match(luad_samples, colnames(luad)) ]
lusc = lusc[ ,match(lusc_samples, colnames(lusc)) ]
luad = luad[ match(goi, rowRanges(luad)$external_gene_name), ]
lusc = lusc[ match(goi, rowRanges(lusc)$external_gene_name), ]
rawdata = cbind(assay(luad), assay(lusc))
coldata = cbind( sampleName = c(colnames(luad), colnames(lusc)),
sampleGroup = c(rep("luad", ncol(luad)), rep("lusc", ncol(lusc)) ))
map = cbind(rownames(rawdata), goi)
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
# a character vector with exactly three elements: the name
# of a factor in the design formula, the name of the
# numerator level for the fold change, and the name of the
# denominator level for the fold change (simplest case)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "luad", "lusc") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = map[match(rownames(resdf), map[,1]), 2],
geneID = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
message( nrow(up_genes), ":down=", nrow(down_genes) )
write.table(resdf, file.path(resdir, "lusc_vs_luad_gdc_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "lusc_vs_luad_gdc_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "lusc_vs_luad_gdc_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### MSKCC
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "lung")
luad = read.delim(file.path(s3_dir, "luad-rsem-count-tcga-t.txt"),
header=T, stringsAsFactors = FALSE, row.names = 1, check.names = FALSE)
lusc = read.delim(file.path(s3_dir, "lusc-rsem-count-tcga-t.txt"),
header=T, stringsAsFactors = FALSE, row.names = 1, check.names = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad_samples= read.delim(file.path(s3_dir,"luad_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
lusc_samples= read.delim(file.path(s3_dir,"lusc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad = luad[ match( goi, rownames(luad)), ]
lusc = lusc[ match( goi, rownames(lusc)), ]
luad = luad[, match( luad_samples, colnames(luad)) ]
lusc = lusc[ ,match( lusc_samples, colnames(lusc)) ]
library(DESeq2)
rawdata = cbind(luad, lusc)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(luad), colnames(lusc)),
sampleGroup = c(rep("luad", ncol(luad)), rep("lusc", ncol(lusc)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "luad", "lusc") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName =rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "lusc_vs_luad_mskcc_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "lusc_vs_luad_mskcc_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "lusc_vs_luad_mskcc_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Recount2
```{r eval = FALSE}
rm(list=ls())
library(SummarizedExperiment)
library("recount")
library(DESeq2)
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
recount_dir = file.path(bigdir, "OriginalTCGAGTExData", "datasource_RECOUNT2_TCGA")
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "lung")
rse_gene = get(load(file.path(recount_dir, "rse_gene.Rdata")))
col_nms = c("gdc_cases.samples.portions.analytes.aliquots.submitter_id",
"cgc_case_batch_number",
"gdc_cases.project.project_id", "auc")
col_idx = match(col_nms, colnames(colData(rse_gene)))
colData(rse_gene) = colData(rse_gene)[, col_idx]
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad_samples= read.delim(file.path(s3_dir,"luad_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
lusc_samples= read.delim(file.path(s3_dir,"lusc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
table(is.na(match( luad_samples, colData(rse_gene)[,1])))
table(is.na(match( lusc_samples, colData(rse_gene)[,1])))
test = sapply(rowRanges(rse_gene)$symbol, function(x) x[1])
row_idx = match(goi, test)
table(is.na(row_idx))
luad = rse_gene[, match( luad_samples, colData(rse_gene)[,1])]
lusc = rse_gene[, match( lusc_samples, colData(rse_gene)[,1])]
luad = luad[ row_idx, ]
lusc = lusc[ row_idx, ]
luad <- scale_counts(luad, round = TRUE)
lusc <- scale_counts(lusc, round = TRUE)
luad = assay(luad)
lusc = assay(lusc)
rownames(luad) = goi
rownames(lusc) = goi
colnames(luad) = luad_samples
colnames(lusc) = lusc_samples
rawdata = cbind(luad, lusc)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(luad), colnames(lusc)),
sampleGroup = c(rep("luad", ncol(luad)), rep("lusc", ncol(lusc)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "luad", "lusc") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "lusc_vs_luad_RECOUNT2_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "lusc_vs_luad_RECOUNT2_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "lusc_vs_luad_RECOUNT2_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Piccolo
```{r eval = FALSE}
rm(list=ls())
library(DESeq2)
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "lung")
data = read.delim(file.path(s3_dir, "GSM1536837_01_27_15_TCGA_20.Illumina.tumor_Rsubread_FeatureCounts.txt"),
header = T, stringsAsFactors = FALSE, row.names = 1, check.names = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad_samples= read.delim(file.path(s3_dir,"luad_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
lusc_samples= read.delim(file.path(s3_dir,"lusc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad = data[, match(luad_samples, colnames(data) )]
lusc = data[, match(lusc_samples, colnames(data) )]
row_idx =match( goi, rownames(data))
luad = luad[ row_idx, ]
lusc = lusc[ row_idx, ]
rawdata = cbind(luad, lusc)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(luad), colnames(lusc)),
sampleGroup = c(rep("luad", ncol(luad)), rep("lusc", ncol(lusc)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "luad", "lusc") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "lusc_vs_luad_Piccolo_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "lusc_vs_luad_Piccolo_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "lusc_vs_luad_Piccolo_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Xena/Toil
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "lung")
data = read.delim(file.path(s3_dir, "OnlyTcga_gene_expected_count"),
header = T, stringsAsFactors = FALSE, row.names = 1,
check.names = FALSE)
map = read.delim(file.path(s3_dir, "gene_map_xena.txt"), header=T, stringsAsFactors = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad_samples= read.delim(file.path(s3_dir,"luad_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
lusc_samples= read.delim(file.path(s3_dir,"lusc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
luad_idx = match(substr(luad_samples, 1, 15), substr(colnames(data), 1, 15) )
lusc_idx = match(substr(lusc_samples, 1, 15), substr(colnames(data), 1, 15) )
row_idx = map[match(goi, map[,2]), 1]
luad = data[, luad_idx]
lusc = data[, lusc_idx]
luad = luad[ row_idx, ]
lusc = lusc[ row_idx, ]
luad =2^luad - 1
lusc =2^lusc - 1
library(DESeq2)
rawdata = cbind(luad, lusc)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(luad), colnames(lusc)),
sampleGroup = c(rep("luad", ncol(luad)), rep("lusc", ncol(lusc)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "lusc", "luad") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = map[match(rownames(resdf), map[,1]), 2],
geneID =rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "lusc_vs_luad_XENA_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "lusc_vs_luad_XENA_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "lusc_vs_luad_XENA_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
## Kidney Comparison
In the next few code chunks, we find DEGs between Kidney renal clear cell carcinoma
(TCGA-KIRC) compared to Kidney renal papillary cell carcinoma(TCGA-KIRP) using DESeq2 from each of the pipelines.
A statistical cutoff of FDR < 0.05 & abs(log2foldchange) >1 was used to find DEGs.
### GDC
```{r eval = FALSE}
rm(list=ls())
library(SummarizedExperiment)
library(DESeq2)
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "kidney")
kirc = get(load(file.path(s3_dir, "gdc_TCGA-KIRC_counts_10_28_2019.Rdata")))
kirp= get(load(file.path(s3_dir, "gdc_TCGA-KIRP_counts_10_28_2019.Rdata")))
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirc_samples= read.delim(file.path(s3_dir,"kirc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirp_samples= read.delim(file.path(s3_dir,"kirp_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirc = kirc[ match(goi, rowRanges(kirc)$external_gene_name), ]
kirp = kirp[ match(goi, rowRanges(kirp)$external_gene_name), ]
kirc = kirc[ ,match(kirc_samples, colnames(kirc)) ]
kirp = kirp[ ,match(kirp_samples, colnames(kirp)) ]
rawdata = cbind(assay(kirc), assay(kirp))
coldata = cbind( sampleName = c(colnames(kirc), colnames(kirp)),
sampleGroup = c(rep("kirc", ncol(kirc)), rep("kirp", ncol(kirp)) ))
map = cbind(rownames(rawdata), goi)
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "kirc", "kirp") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName = map[match(rownames(resdf), map[,1]), 2],
geneID = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "kirp_vs_kirc_gdc_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "kirp_vs_kirc_gdc_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "kirp_vs_kirc_gdc_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### MSKCC
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "kidney")
kirc = read.delim(file.path(s3_dir, "kirc-rsem-count-tcga-t.txt"),
header=T, stringsAsFactors = FALSE, row.names = 1, check.names = FALSE)
kirp = read.delim(file.path(s3_dir, "kirp-rsem-count-tcga-t.txt"),
header=T, stringsAsFactors = FALSE, row.names = 1, check.names = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirc_samples= read.delim(file.path(s3_dir,"kirc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirp_samples= read.delim(file.path(s3_dir,"kirp_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirc = kirc[ match( goi, rownames(kirc)), ]
kirp = kirp[ match( goi, rownames(kirp)), ]
kirc = kirc[, match( kirc_samples, colnames(kirc)) ]
kirp = kirp[ ,match( kirp_samples, colnames(kirp)) ]
library(DESeq2)
rawdata = cbind(kirc, kirp)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(kirc), colnames(kirp)),
sampleGroup = c(rep("kirc", ncol(kirc)), rep("kirp", ncol(kirp)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "kirc", "kirp") )
resdf = as.data.frame(resdf)
resdf = cbind(geneName =rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
message( nrow(up_genes), ":down=", nrow(down_genes) )
write.table(resdf, file.path(resdir, "kirp_vs_kirc_mskcc_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "kirp_vs_kirc_mskcc_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "kirp_vs_kirc_mskcc_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Recount2
```{r eval = FALSE}
rm(list=ls())
library(SummarizedExperiment)
library("recount")
library(DESeq2)
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
recount_dir = file.path(bigdir, "OriginalTCGAGTExData", "datasource_RECOUNT2_TCGA")
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "kidney")
rse_gene = get(load(file.path(recount_dir, "rse_gene.Rdata")))
col_nms = c("gdc_cases.samples.portions.analytes.aliquots.submitter_id",
"cgc_case_batch_number",
"gdc_cases.project.project_id", "auc")
col_idx = match(col_nms, colnames(colData(rse_gene)))
colData(rse_gene) = colData(rse_gene)[, col_idx]
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirc_samples= read.delim(file.path(s3_dir,"kirc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirp_samples= read.delim(file.path(s3_dir,"kirp_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
test = sapply(rowRanges(rse_gene)$symbol, function(x) x[1])
row_idx = match(goi, test)
kirc = rse_gene[, match( kirc_samples, colData(rse_gene)[,1])]
kirp = rse_gene[, match( kirp_samples, colData(rse_gene)[,1])]
kirc = kirc[ row_idx, ]
kirp = kirp[ row_idx, ]
kirc <- scale_counts(kirc, round = TRUE)
kirp <- scale_counts(kirp, round = TRUE)
kirc = assay(kirc)
kirp = assay(kirp)
rownames(kirc) = goi
rownames(kirp) = goi
colnames(kirc) = kirc_samples
colnames(kirp) = kirp_samples
rawdata = cbind(kirc, kirp)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(kirc), colnames(kirp)),
sampleGroup = c(rep("kirc", ncol(kirc)), rep("kirp", ncol(kirp)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "kirc", "kirp" ))
resdf = as.data.frame(resdf)
resdf = cbind(geneName = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "kirp_vs_kirc_RECOUNT2_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "kirp_vs_kirc_RECOUNT2_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "kirp_vs_kirc_RECOUNT2_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Piccolo
```{r eval = FALSE}
rm(list=ls())
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData", "raw_counts")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data/de_analysis
resdir = file.path(bigdir, "data", "de_analysis", "kidney")
data = read.delim(file.path(s3_dir, "GSM1536837_01_27_15_TCGA_20.Illumina.tumor_Rsubread_FeatureCounts.txt"),
header = T, stringsAsFactors = FALSE, row.names = 1,
check.names = FALSE)
goi= read.delim(file.path(s3_dir, "de_goi.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirc_samples= read.delim(file.path(s3_dir,"kirc_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirp_samples= read.delim(file.path(s3_dir,"kirp_samples.txt"), header=FALSE, stringsAsFactors = FALSE )[,1]
kirc = data[, match(kirc_samples, colnames(data) )]
kirp = data[, match(kirp_samples, colnames(data) )]
row_idx =match( goi, rownames(data))
kirc = kirc[ row_idx, ]
kirp = kirp[ row_idx, ]
library(DESeq2)
rawdata = cbind(kirc, kirp)
rawdata = round(rawdata, 0)
coldata = cbind( sampleName = c(colnames(kirc), colnames(kirp)),
sampleGroup = c(rep("kirc", ncol(kirc)), rep("kirp", ncol(kirp)) ))
dds <- DESeqDataSetFromMatrix(countData = rawdata,
colData = coldata, design = ~ sampleGroup)
dds = DESeq(dds)
lfc = log2(2)
resdf <- results(dds, alpha = 0.05, lfcThreshold=lfc, contrast = c("sampleGroup", "kirc", "kirp" ))
resdf = as.data.frame(resdf)
resdf = cbind(geneName = rownames(resdf), resdf)
up_genes = resdf[ which(resdf$log2FoldChange > lfc & resdf$padj < 0.05), ]
down_genes = resdf[which(resdf$log2FoldChange < -lfc & resdf$padj < 0.05), ]
write.table(resdf, file.path(resdir, "kirp_vs_kirc_Piccolo_deseq2_all_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(up_genes, file.path(resdir, "kirp_vs_kirc_Piccolo_deseq2_up_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
write.table(down_genes, file.path(resdir, "kirp_vs_kirc_Piccolo_deseq2_down_reg_genes.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
```
### Xena/Toil
```{r eval = FALSE}
rm(list=ls())