We have a very simple experiment, 1 control (C) and 3 independent treatments (T, B, A). So technically I wrote the following script thinking it would compare C to T, then C to B and finally C to A.
Though at the bottom of my pipeline, it seems that DESeq2 is comparing treatments T vs A. Is there a better way to write the condition so it would compare independentely control to each treatment ? (After that I should cluster genes and output a heatmap)
# DESeq1 libraries
library( "DESeq2" )
library("Biobase")
# Heatmap libraries
library( "genefilter" )
library(gplots)
r2 = read.table("matrix.txt", header=TRUE, row.names=1)
head(r2)
samples <- data.frame(row.names=c("C1", "T1", "B1", "A1"), condition=as.factor(c("C1", "T1", "B1", "A1")))
dds <- DESeqDataSetFromMatrix(countData = as.matrix(r2), colData=samples, design=~condition)
# Run DESeq2
dds <- DESeq(dds)
# DESeq2 Results
res <- results(dds)
head (res)
**********
mcols(res, use.names=TRUE)
> mcols(res, use.names=TRUE)
DataFrame with 6 rows and 2 columns
type description
<character> <character>
baseMean intermediate the base mean over all rows
log2FoldChange results log2 fold change (MAP): condition T1 vs A1
lfcSE results standard error: condition T1 vs A1
stat results Wald statistic: condition T1 vs A1
pvalue results Wald test p-value: condition T1 vs A1
padj results BH adjusted p-values