Hi all,

I have performed DESeq2 analysis on my samples which consist of 5 biological replicates under control and cytokine-treated conditions. Of the 18435 genes run through DESeq2, 2517 meet the threshold of padj<0.05 and a log2 fold change >1 (increase/decrease).

My data matrix is called vst_heatmap_mat_filtered

> dim(vst_heatmap_mat_filtered)
[1] 2517   10

The code used to generate the heatmap and the heatmap itself are shown below

col = colorRampPalette(rev(brewer.pal(n = 10, name = "RdYlBu")))(100)
group = data.frame(Condition = rep(c("Control", "IL-1β"), c(5,5))) 
row.names(group) = colnames(vst_heatmap_mat_filtered)

Condition = c("navy", "darkgreen")
names(Condition) = c("Control", "IL-1β")
anno_colors = list(Condition = Condition)

pheatmap(vst_heatmap_mat_filtered, scale = 'row', 
         fontsize_row = 1, 
         fontsize_col = 8, 
         color = col,
         annotation_col = group,
         annotation_colors = anno_colors,
         cluster_rows = T,
         cutree_rows = 2,
         cluster_cols = F)

I reduced the amount of genes to 30 of my most differentially expressed genes, which I selected based on the log2 fold change magnitude (as all had significant padj values). So top 15 upregulated and top 15 downregulated in response to the cytokine treatment.

> dim(filterTop15Downregulated)
[1] 15  6

> dim(filterTop15Upregulated)
[1] 15  6

I combined the two with rbind

> dim(Top30)
[1] 30  6

Then I performed the same sequential steps as with the original data.frame to end up with a vst matrix of 30 genes

> dim(Top30_vst)
[1] 30 10

And the heatmap looks like this

I didn't cluster by column as my treatment samples clustered to the left, so I only clustered by row.

I would like to follow this up with additional heatmaps of the top 50 and 100 most differentially expressed genes, but I'm not sure if this is the best/most efficient way to extract/depict the data. Any suggestions?

Thanks in advance!