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DESeq2_pipeline.Rmd
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DESeq2_pipeline.Rmd
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---
title: "DESeq2_pipeline"
author: "Sreedevi Kesavan"
date: "25/04/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#### Library Install
```{r eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
BiocManager::install("edgeR")
BiocManager::install("limma")
BiocManager::install("tximport")
BiocManager::install("tximportData") # this is relatively big
BiocManager::install("tximeta")
BiocManager::install("GenomeInfoDb")
BiocManager::install("org.Dm.eg.db")
BiocManager::install("TxDb.Mmusculus.UCSC.mm10.ensGene")
BiocManager::install("rhdf5")
BiocManager::install("Rsubread")
```
```{r warning=FALSE, message=FALSE, output=FALSE}
library(TxDb.Mmusculus.UCSC.mm10.ensGene)
library(DESeq2)
library(tximport)
library(readr)
library(RColorBrewer)
library(gplots)
library(rhdf5)
library(Rsubread)
library(tidyverse)
library(biomaRt)
```
#### Loading Mouse gene set
```{r warning=FALSE message=FALSE output=FALSE}
txdb <- TxDb.Mmusculus.UCSC.mm10.ensGene
k <- keys(txdb, keytype = "TXNAME")
tx2gene <- select(x = txdb, keys = k, "GENEID", "TXNAME")
head(tx2gene)
```
### Counting HiSAT2 data with featurCounts
```{r}
bam_files <- file.path("bam_filter", list.files("bam_filter"))
# check that the files actually exist. Should return TRUE for each file
file.exists(bam_files)
# Let's take a look at this variable we have created
bam_files
str_counts <- featureCounts(bam_files, annot.ext = 'mm10.ref.gtf', isGTFAnnotationFile = TRUE, GTF.featureType = 'gene', GTF.attrType = 'gene_id', countMultiMappingReads = TRUE)
write.csv(str_counts[["counts"]], 'str_counts.csv')
countData <- read.csv('str_count.csv', header = TRUE)
countName <- c("id",
"day14_immuno_treat_1",
"day14_immuno_treat_2",
"day14_immuno_treat_3",
"day14_immuno_treat_4",
"day14_immuno_treat_5",
"day14_isotype_control_1",
"day14_isotype_control_2",
"day14_isotype_control_3",
"day14_isotype_control_4",
"day14_isotype_control_5",
"day7_immuno_treat_1",
"day7_immuno_treat_2",
"day7_immuno_treat_3",
"day7_immuno_treat_4",
"day7_immuno_treat_5",
"day7_isotype_control_1",
"day7_isotype_control_2",
"day7_isotype_control_3",
"day7_isotype_control_4",
"day7_isotype_control_5")
names(countData) <- countName
countData$id <- gsub("\\..*", "",countData$id)
# Experimental Setup
Sample <- c("day14_immuno_treat_1",
"day14_immuno_treat_2",
"day14_immuno_treat_3",
"day14_immuno_treat_4",
"day14_immuno_treat_5",
"day14_isotype_control_1",
"day14_isotype_control_2",
"day14_isotype_control_3",
"day14_isotype_control_4",
"day14_isotype_control_5",
"day7_immuno_treat_1",
"day7_immuno_treat_2",
"day7_immuno_treat_3",
"day7_immuno_treat_4",
"day7_immuno_treat_5",
"day7_isotype_control_1",
"day7_isotype_control_2",
"day7_isotype_control_3",
"day7_isotype_control_4",
"day7_isotype_control_5")
dpi <- c(rep(14, 10), rep(7, 10))
condition <- c(rep('Treatment', 5), rep('Control', 5), rep('Treatment', 5), rep('Control', 5))
group <- c("day14_treat",
"day14_treat",
"day14_treat",
"day14_treat",
"day14_treat",
"day14_control",
"day14_control",
"day14_control",
"day14_control",
"day14_control",
"day7_treat",
"day7_treat",
"day7_treat",
"day7_treat",
"day7_treat",
"day7_control",
"day7_control",
"day7_control",
"day7_control",
"day7_control")
metaData<- cbind(Sample, dpi, condition, group)
dds <- DESeqDataSetFromMatrix(countData=countData,
colData=metaData,
design=~group, tidy = TRUE)
dds$group <- relevel(dds$group, ref = "day7_control")
hisat_final <- DESeq(dds)
hisat_day7_comp <- results(hisat_final, contrast = list('group_day7_treat_vs_day7_control'),
independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)
# dds$group <- relevel(dds$group, ref = "day14_control")
# hisat_final <- DESeq(dds)
#
# hisat_day14_comp <- results(hisat_final, contrast = list('group_day14_treat_vs_day14_control'),
# independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)
# write.csv(hisat_day14_comp, "hisat_day14.csv")
write.csv(hisat_day7_comp, "hisat_day7.csv")
```
#### Data download Salmon and Kallisto
```{r warning=FALSE message=FALSE output=FALSE}
# Salmon quants ----
quant_files <- file.path("salmon_counts", list.files("salmon_counts"), "quant.sf")
# check that the files actually exist. Should return TRUE for each file
file.exists(quant_files)
# Let's take a look at this variable we have created
quant_files
txi_salmon <- tximport(quant_files,
type = "salmon", tx2gene = tx2gene)
```
```{r warning=FALSE message=FALSE output=FALSE}
kal_files <- file.path("kallisto_counts", list.files("Analysis Output/kallisto_counts"), "abundance.tsv")
# check that the files actually exist. Should return TRUE for each file
file.exists(kal_files)
# Let's take a look at this variable we have created
kal_files
txi_kallisto <- tximport(kal_files,
type = "kallisto", tx2gene = tx2gene)
```
```{r warning=FALSE message=FALSE output=FALSE}
# Names of samples.
samples <- c("day14_immuno_treat_1",
"day14_immuno_treat_2",
"day14_immuno_treat_3",
"day14_immuno_treat_4",
"day14_immuno_treat_5",
"day14_isotype_control_1",
"day14_isotype_control_2",
"day14_isotype_control_3",
"day14_isotype_control_4",
"day14_isotype_control_5",
"day7_immuno_treat_1",
"day7_immuno_treat_2",
"day7_immuno_treat_3",
"day7_immuno_treat_4",
"day7_immuno_treat_5",
"day7_isotype_control_1",
"day7_isotype_control_2",
"day7_isotype_control_3",
"day7_isotype_control_4",
"day7_isotype_control_5")
names(quant_files) <- samples
names(kal_files) <- samples
```
#### Experimental Design Salmon
```{r warning=FALSE message=FALSE output=FALSE}
rna.design <- data.frame(sample=samples,
file=quant_files,
dpi=dpi,
condition=condition,
group=group)
rna.design.2 <- data.frame(sample=samples,
file=quant_files,
dpi=dpi,
condition=condition)
load.model<- model.matrix(~group)
model_salmon <- DESeqDataSetFromTximport(txi_salmon,
rna.design, design=load.model)
out_salmon <- DESeq(model_salmon)
resultsNames(out_salmon)
salmon_day7_comp <- results(out_salmon, contrast = list('groupday7_control', 'groupday7_treat'),
independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)
# rna.design$group <- factor(rna.design$group, levels=c('day14_control', 'day14_treat', 'day7_control', 'day7_treat'))
# model_salmon <- DESeqDataSetFromTximport(txi_salmon,
# rna.design, design=load.model)
# out_salmon <- DESeq(model_salmon)
# resultsNames(out_salmon)
# salmon_day14_comp <- results(out_salmon, contrast = list('groupday14_control', 'groupday14_treat'),
# independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)
# write.csv(salmon_day14_comp, "salmon_day14.csv")
write.csv(salmon_day7_comp, "salmon_day7.csv")
```
#### Experimental Design Kallisto
```{r warning=FALSE message=FALSE output=FALSE}
rna.design <- data.frame(sample=samples,
file=kal_files,
dpi=dpi,
condition=condition,
group=group)
load.model<- model.matrix(~group)
model_kal <- DESeqDataSetFromTximport(txi_kallisto,
rna.design, design=load.model)
out_kal <- DESeq(model_kal)
resultsNames(out_kal)
kal_day7_comp <- results(out_kal, contrast = list('groupday7_control', 'groupday7_treat'),
independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)
# rna.design$group <- factor(rna.design$group, levels=c('day14_control', 'day14_treat', 'day7_control', 'day7_treat'))
# model_kal <- DESeqDataSetFromTximport(txi_salmon,
# rna.design, design=load.model)
# out_kal <- DESeq(model_kal)
# resultsNames(out_salmon)
# kal_day14_comp <- results(out_kal, contrast = list('groupday14_control', 'groupday14_treat'),
# independentFiltering=TRUE, alpha=0.05, pAdjustMethod="BH", parallel=TRUE)
# write.csv(kal_day14_comp, "kal_day14.csv")
write.csv(kal_day7_comp, "kal_day7.csv")
```
```{r}
par(mfrow = c(3,1))
k <- plotMA(kal_day7_comp) + title('Kallisto')
s <- plotMA(salmon_day7_comp) + title('Salmon')
h <- plotMA(hisat_day7_comp) + title('HISAT2')
```