Omics data: How to interpret heatmap and dendrogram output?

Question

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 01:07
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

score 3 · Accepted Answer · answered Jul 30 '18 at 07:43

The dendrogram summarize the information of a group of values and sort them according to the similarity they have. It can be applied to both, samples and features.

The dendrogram allows to visualize features that are more similar together, usually revealing patterns that wouldn't have been seen otherwise.

In an article usually it is used something like: "we can see that X and Y are clustered together revealing that ... (they have something in common), while Z and A are clustered far"

GIM · Answer 2 · 2018-07-31T22:13:24.550

3

You may be interested in reading up on heatmaps. For a history perspective (pre the biological introduction by Eisen et al. ) read The History of the Cluster Heat Map by Wilkinson and Friendly

edited Jul 31 '18 at 22:13

answered Jul 31 '18 at 12:16

GIM

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Omics data: How to interpret heatmap and dendrogram output?

2 Answers2