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)  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)  15 6 > dim(filterTop15Upregulated)  15 6
I combined the two with
> dim(Top30)  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)  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!