[BioC] heatmap.2 function and the scale option

[john herbert]

Dear Ales,
Thank you for your reply. If I ask myself the same question, I can see
there is a lot of variation between groups. So, although I have 12
subjects per group and hence significant biological replicates, I am
only finding one or two significant deferentially expressed genes. So
the variability is looking like the reason I don't get nice heatmaps.

The information I have is like log2 fold change. So the the CT of a
gene, minus the CT of the control gene and this value to log2 scale. I
think it is similar to log 2 fold change like MA in microarray.




2011/10/5 Aleš Maver <ales.maver at gmail.com>:
> Hi!
> I think it depends on what information you have stored in rows and what in
> columns. It also depends what data you have stored in the matrix.
> For example, if your input values are deltaCt values (you mentioned you work
> on RT-PCR results), they may differ substantially across genes, which may
> cause your heatmap to look anomalous.
>
> If you look at an exemplar matrix with dCt values for three genes in rows
> (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),
> sep="")))
>
> Here the distributions of measured values differ substantially between genes
> (means 5,10 and 15). If you inspect the plot where scaling across columns is
> used:
> heatmap(mat, scale="column")
>
> you will notice Gene3 to be always more yellow in color than Gene1 or Gene2,
> 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 calculated for
> each gene (row) seperately:
> heatmap(mat, scale="row")
>
> Hope this explains it,
> Ales
>
> 2011/10/3 john herbert <arraystruggles at gmail.com>
>>
>> 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
>> blotchy.
>>
>> 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,
>>
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>
>
>
> --
> Ales Maver, MD
> Institute of Medical Genetics, Department of Obstetrics and Gynaecology
> UMC Ljubljana
> Šlajmerjeva 3
> SI-1000 Ljubljana
> Slovenia
>