We have whole transcriptome data and used deseq2 to determine differentially expressed genes. I am able to run deseq2 up to drawing heatmaps in R with gplot and heatmap.2 library. Though I would like to do two more things:

• Group differentially expressed genes by pathways, for example in a pool of 100 DEG, cluster them by their biological process GO term.

AND/OR

• Among DE genes in my matrix, only select a few of interest (let say 10) and display them in a separate heatmap. Since genes are raw names, I understand they should be in a separate vector, though I have no clue how to do that..

Our dataset looks like that with gene name in row and samples in column, read_count is the content:

Gene_name Sample1 Sample2 Sample3  [...]     
Gene1          10      11    1000
Gene2        1000      12       1
Gene3        2999    2222       1

Here is how I used Deseq2 and built heatmaps from it:

# Define samples/control
samples <- data.frame(row.names=c("sample1”, "sample2”, "sample3”, "tumor1”, "tumor2”, "tumor3”), condition=as.factor(c(rep("sample",3), rep("tumor", 3))))
samples$condition <- relevel(samples$condition, "control")

# Launch DESeq2
dds <- DESeqDataSetFromMatrix(...etc..)
dds <- DESeq(dds, betaPrior=FALSE)
rld <- rlogTransformation(dds)

DEgenes = ... as per our criteria

# Heatmap
[define heatmap size etc.] 

hmcol<- colorRampPalette( rev(brewer.pal(9, "RdBu")))(255)
heatmap.2( assay(rld)[DEgenes, ], Colv=FALSE, scale="row", 
trace="none", dendrogram="row", key = FALSE,  lmat=lmat, lhei=lhei, lwid=lwid,
col = hmcol, margin=c(4, 10), cexCol = 1)