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Dasen_RNAseq_report_brachial_padj_nologFCcutoff.Rmd
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Dasen_RNAseq_report_brachial_padj_nologFCcutoff.Rmd
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---
title: "Dasen lab, Pbx-mutant RNAseq, brachial"
author: "Lisa Cohen"
output: html_document
---
# Brachial-mutant vs. Control
Filenames containing raw transcript counts from htseq-count:
```{r,echo=FALSE, message=FALSE, warning=FALSE}
library(DESeq2)
library("genefilter")
library(gplots)
library(RColorBrewer)
library(biomaRt)
library("genefilter")
library("lattice")
source('~/Documents/scripts/plotPCAWithSampleNames.R')
setwd("~/Documents/NYUMC/Dasen/brachial/htseq_counts")
mypath<-"~/Documents/NYUMC/Dasen/brachial/htseq_counts"
filenames<-list.files(path=mypath, pattern= "_counts.txt", full.names=FALSE)
datalist <-lapply(filenames, function(x){read.table(x,header=FALSE, sep="\t")})
for (i in 1:length(filenames))
{
colnames(datalist[[i]])<-c("ID",filenames[[i]])
}
mergeddata <- Reduce(function(x,y) {merge(x,y, by="ID")}, datalist)
new_data_merge<-mergeddata[-1:-5,]
#write.csv(new_data_merge,file="Dasen_thoracic_count_data_Ensembl.csv")
rown<-new_data_merge$ID
rownames(new_data_merge)<-rown
new_data_merge<-new_data_merge[,-1]
data<-new_data_merge
colnames(data)
col.names<-c("BR-A-Control","BR-A-Mutant","BR-B-Control","BR-B-Mutant","BR-C-Control","BR-C-Mutant")
colnames(data)<-col.names
ExpDesign <- data.frame(row.names=colnames(data), condition = c("Control","Mutant","Control","Mutant","Control","Mutant"))
cds<-DESeqDataSetFromMatrix(countData=data, colData=ExpDesign,design=~condition)
cds$condition <- relevel(cds$condition, "Control")
cds<-DESeq(cds,betaPrior=FALSE)
# get norm counts
norm_counts<-counts(cds,normalized=TRUE)
norm_counts_data<-as.data.frame(norm_counts)
ensembl_id<-rownames(norm_counts)
norm_counts_data<-cbind(ensembl_id,norm_counts_data)
filtered_norm_counts<-norm_counts_data[!rowSums(norm_counts_data[,2:7]==0)>=1, ]
```
The size of the table with all transcripts is:
```{r,echo=FALSE, message=FALSE, warning=FALSE}
# get gene name from Ensembl gene ID
ensembl=useMart("ensembl")
ensembl = useDataset("mmusculus_gene_ensembl",mart=ensembl)
data_table<-filtered_norm_counts
query<-getBM(attributes=c('ensembl_gene_id','external_gene_name','gene_biotype'), filters = 'ensembl_gene_id', values = ensembl_id, mart=ensembl)
col.names<-c("ensembl_id","external_gene_id","gene_biotype")
colnames(query)<-col.names
merge_biomart_res_counts <- merge(data_table,query,by="ensembl_id")
temp_data_merged_counts<-merge_biomart_res_counts
##
res<-results(cds,contrast=c("condition","Mutant","Control"))
res_ordered<-res[order(res$padj),]
ensembl_id<-rownames(res_ordered)
res_ordered<-as.data.frame(res_ordered)
res_ordered<-cbind(res_ordered,ensembl_id)
merge_biomart_res_counts <- merge(temp_data_merged_counts,res_ordered,by="ensembl_id")
merge_biomart_res_all<-subset(merge_biomart_res_counts,merge_biomart_res_counts$padj!="NA")
merge_biomart_res_all<-merge_biomart_res_all[order(merge_biomart_res_all$padj),]
dim(merge_biomart_res_all)
write.csv(new_data_merge,file="Dasen_brachial_Mutant_v_Control_CPM_all.csv")
```
The size of the table with only significant transcripts, padj<0.05 is:
```{r,echo=FALSE, message=FALSE, warning=FALSE}
res_merged_cutoff<-subset(merge_biomart_res_all,merge_biomart_res_all$padj<0.05)
dim(res_merged_cutoff)
plot(log2(res$baseMean), res$log2FoldChange, col=ifelse(res$padj < 0.05, "red","gray67"),main="(DESeq2) Brachial Mutant vs. Control (padj<0.05)",xlim=c(1,15),pch=20,cex=1,ylim=c(-5,5))
abline(h=c(-1,1), col="blue")
write.csv(new_data_merge,file="Dasen_brachial_Mutant_v_Control_CPM_padj0.05.csv")
```
# Heatmap
All genes padj<0.05, regardless of FC (all red points from MA plot above)
```{r,echo=FALSE, message=FALSE, warning=FALSE}
#up_down_1FC<-subset(res_merged_cutoff,res_merged_cutoff$log2FoldChange>1 | res_merged_cutoff$log2FoldChange< -1)
#d<-as.matrix(up_down_1FC[,c(2:7)])
#rownames(d) <- up_down_1FC[,8]
dim(res_merged_cutoff)
d<-as.matrix(res_merged_cutoff[,c(2:7)])
rownames(d)<-res_merged_cutoff[,8]
d<-na.omit(d)
d<-d[,c(1,3,5,2,4,6)]
colnames(d)<-c("BR-A-Control","BR-B-Control","BR-C-Control","BR-A-Mutant","BR-B-Mutant","BR-C-Mutant")
hr <- hclust(as.dist(1-cor(t(d), method="pearson")), method="complete")
mycl <- cutree(hr, h=max(hr$height/1.5))
clusterCols <- rainbow(length(unique(mycl)))
myClusterSideBar <- clusterCols[mycl]
myheatcol <- greenred(75)
#tiff("Brachial_heatmap.tiff", width = 1000,height = 1000,units="px",res = NA,pointsize=12)
heatmap.2(d,
Rowv=as.dendrogram(hr),
cexRow=0.8,cexCol=0.8,srtCol= 90,
adjCol = c(NA,0),offsetCol=2.5,offsetRow=0.001,
Colv=NA, dendrogram="row",
scale="row", col=myheatcol,
density.info="none",
trace="none")
#dev.off()
###
```
Versions:
```{r,}
sessionInfo()
```
### Sequencing and bioinformatics analysis by:
NYU Langone Medical Center
Bioinformatics Core, Genome Technology Center, OCS
Email: Genomics@nyumc.org
Phone: 646-501-2834
http://ocs.med.nyu.edu/bioinformatics-core
http://ocs.med.nyu.edu/genome-technology-center
# References
M. I. Love, W. Huber, S. Anders: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
Genome Biology 2014, 15:550. http://dx.doi.org/10.1186/s13059-014-0550-8
R-Bioconductor: http://www.bioconductor.org/
DESeq2: http://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf