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I plot the same set of data with heatmap and heatmap.2 respectively, and got distinctly different profiles. Anybody could explain this? I know heatmap.2 has extra functions, such as color key; but it looks that its imaging algorithm might also be quite different from heatmap. Is it true? Thanks.
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I noticed the same thing some time ago...
heatmap does not use the same colorkey for every row by default. Instead it performs some weird scaling...
This essentially means that the rows are NOT comparable by means of colour, since the scaling is performed independently for every row.
you can try this very easily with the following commands:
x = c(1:10)
a = cbind(x,x,x,x)
a[1,2] = 100
a[1,3] = 1000
heatmap(a)
Here's what the help says:
"By default (scale = "row") the rows are scaled to have mean zero and standard deviation one. There is some empirical evidence from genomic plotting that this is useful. "
set scale = "none" and your results will be comparable.
heatmap(a,scale="none")
heatmap.2(a,trace="none")
Best regards,
René -
Thanks for help
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...06-18-2026, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
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