Question: heatmap.2 row clustering does not look right
0
gravatar for moxu
16 months ago by
moxu430
moxu430 wrote:

Heatmap Example

Image is at https://imgur.com/CDzSiLU

I am using R heatmap.2.

Take a look at the above heatmap generated by heatmap.2. There are 5 big clusters of rows: 1 yellow/red, 2 red/yellow, 3 yellow/red, 4 red/yellow, 5 yellow/red.

Shouldn't the yellow/red clusters cluster together, and red/yellow ones cluster together, and then form the biggest cluster?

The R code is as follows:

heatmap.2(data.matrix(heatmapEntries), main="xxx", dendrogram="row", Rowv = TRUE, Colv=FALSE, margins=c(11.3333333333333,5), scale="row", trace = "none", cexRow=1, lhei=c(1,10), lwid=c(1,3));

Thanks in advance!

rna-seq next-gen R software error • 1.4k views
ADD COMMENTlink modified 16 months ago by Kevin Blighe41k • written 16 months ago by moxu430
3
gravatar for Kevin Blighe
16 months ago by
Kevin Blighe41k
Guy's Hospital, London
Kevin Blighe41k wrote:

Not necessarily, but you can modify the ordering of the heatmap with the reorderfun parameter to heatmap.2. For example, try:

heatmap.2(..., reorderfun=function(d,w) reorder(d, w, agglo.FUN=mean), ...)

You can choose any mathematical parameter here, including min, max, var, sd, etc.


You can also modify the scaling to see how that modifies the heatmap ordering. For example, to scale to Z-scores, use:

heatZ <- t(scale(t(heatmapEntries)))

heatmap.2(data.matrix(heatZ), ..., scale="none", reorderfun=function(d,w) reorder(d, w, agglo.FUN=mean), ...)

For other suggestions, see my answer here: A: How to cluster the upregulated and downregulated genes in heatmap?

ADD COMMENTlink written 16 months ago by Kevin Blighe41k

Scaling is the answer! Thanks much!

ADD REPLYlink written 16 months ago by moxu430

Okay, but, as per the other thread, scaling is usually combined with a 'breaks' parameter:

heatZ <- t(scale(t(heatmapEntries)))
myBreaks <- seq(-3, 3, length.out=101)
heatmap.2(data.matrix(heatZ), ..., breaks=myBreaks, scale="none", reorderfun=function(d,w) reorder(d, w, agglo.FUN=mean), ...)

Also important to realise that the heatmap / hierarchical clustering is just for the purposes of visualisation. The genes that you supplied to your heatmap would have presumably been derived from expression data on a different scale (if RNA-seq, most like negative binomial).

ADD REPLYlink written 16 months ago by Kevin Blighe41k
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