Making a heatmap: sample vs. fold change
I often see in RNA-Seq papers a figure that plots samples on the Y axis, genes on the X axis, and a fill color representing fold change. Here is an example.
The DESeq package explains how to make a heatmap of normalized expression, but not of fold change. I notice that DESeq sometimes steers its users towards best practices by programming in one approach into their packages while leaving out less desirable approaches (e.g. using dist() vs. cor() in its sample-sample clustering heatmap). Does this imply that there is something less-than-ideal about plotting fold changes for a heatmap to visualize patterns of gene expression across samples?
If it is totally fine to plot fold changes – how does one calculate these FCs, since DEseq itself wouldn’t provide it? For example, you could:
For gene X in sample Y, divide that expression value by the mean expression value of Gene X across ALL samples;
For gene X in sample Y, divide that expression value by the mean expression value of Gene X across CONTROL samples only;
…and so forth.
I’m guessing #1 is best practice, but I’m not sure if there is a convention or best practice associated with this kind of visualization.
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