I think it depends on what information you have stored in rows and
columns. It also depends what data you have stored in the matrix.
For example, if your input values are deltaCt values (you mentioned
on RT-PCR results), they may differ substantially across genes, which
cause your heatmap to look anomalous.
If you look at an exemplar matrix with dCt values for three genes in
(Gene1-Gene3) and ten samples in columns:
mat<-matrix(c(rnorm(n=10, mean=5, sd=1),
rnorm(n=10, mean=6, sd=1),
rnorm(n=10, mean=15, sd=1)), ncol=10, byrow=T,
dimnames=list(paste("Gene", c(1:3), sep=""), paste("Sample", c(1:10),
Here the distributions of measured values differ substantially between
(means 5,10 and 15). If you inspect the plot where scaling across
you will notice Gene3 to be always more yellow in color than Gene1 or
as Gene3 values are always more than other two.
However, using scaling across rows brings out the differences between
samples - separately for each gene (row), as value range is
each gene (row) seperately:
Hope this explains it,
2011/10/3 john herbert <firstname.lastname@example.org>
> Dear Bioconductors,
> I am using the the heatmap.2 function to plot out a heatmap of 130
> genes from Q-PCR expression.
> If I plot them out scaling by row, I get a heatmap that looks a bit
> However, if I scale by column, the heatmap looks a lot better.
> The clustering of samples is the same but I don't really know the
> theory behind why column scaling looks better than row scaling.
> Please can someone advise me on any theory behind this?
> The data is in a matrix, like microarray, with columns of different
> conditions and rows of genes.
> Thank you,
> Bioconductor mailing list
> Search the archives:
Ales Maver, MD
Institute of Medical Genetics, Department of Obstetrics and
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