# Omics data: How to interpret heatmap and dendrogram output?

How to interpret heat map and dendrogram output for biological data (omics) in words (when writing results and discussion)?

What should I consider (statistics behind?) and what is the best approach?

Here is one of my HM for proteomics data.

Script being used

col <- colorRampPalette(c("red","yellow","darkgreen"))(30)
png("HM1.png")
heatmap.2(as.matrix(df), Rowv = T, Colv = FALSE, dendrogram = "row", #scale = "row", col = col, density.info = "none", trace = "none", margins = c(7, 15) )
dev.off()


Heat Map output.

I would be greatful if you would give an example.

• Why are you producing the heatmap in the first place? What question are you trying to answer? – gringer Jul 29 '18 at 1:07
• Did you read the original paper on clustering by Eisen et al. in 1998? What have you tried already to understand more about why other people use heatmap clustering? – benn Jul 29 '18 at 11:54