How to regroup columns in R using heatmap.2 or pheatmap ?

Question

[Björn]

Using following heatmap.2 command

heatmap.2(highly_variable_lcpm,labRow = y2$genes$genes, Rowv = TRUE, col=rev(morecols(20)),trace="none",tracecol = "black",main="Top 20 most variable genes across samples",cex.main=0.6, ColSideColors=group.col,scale="row",margins=c(10,9))

I got the following graph ![heatmap][1][1]: https://ibb.co/iiN257 The colors in column header indicate similar group which are 5 different colors (groups). How to make the samples in each group together so as to show group-wise variation (which is supervised clustering). Thanks

[russhh]

If your input samples are in the correct order, use dendrogram="row" to turn off the column-wise clustering

[Buffo]

or, if your input matrix is ordered try Rowv=False

[Björn]

The row is not in order so adding

Colv=FALSE, dendrogram="row"

did not really help . E.g. my samples are as A A A B B B A A A C C C N N N. I need to find a way to bring all 6 "A" together.

[russhh]

Have you considered then reordering them

[Björn]

That was my question , how ?

[Kevin Blighe]

Re-order them in your actual data-matrix, i.e., before you even apply the heatmap.2 function, and then use Colv and dendrogram as per Buffo

Something like:

sample.order <- A A A B B B A A A C C C N N N *pseudocode
highly_variable_lcpm.ordered <- highly_variable_lcpm[,sample.order]

heatmap.2(highly_variable_lcpm.ordered, ...)

Answer 1

[h.mon]

Reorder the matrix by column names:

data <- data[ , order( colnames( data ) ) ]

Then plot the heatmap with Colv=FALSE, dendrogram="row".

I wouldn't try to force the groupings, though, if your treatments are not clustering, this reflects the structure of the data and is important to the interpretation of the results.