as WouterDeCoster says QN might be possible for RNA-seq but is not common. I suggest reading the manuals of e.g.
DESeq2 to learn about normalization. Aditionally check the videos linked below which nicely explain the normalization techniques that are part of the differential pipeline of these two tools. Beyond that
DESeq2 offers two functions,
rlog that not only normalize counts with respect to library size and composition but also try to unlock the variance dependency from the mean. If these vocabulary are new to you search around in the web, there is plenty of forum and blog entries on normalization and RNA-seq available.
I suggest you use one of the mentioned packages for differential analysis (normalization will be taken care of internally) and
vst for everything else (e.g. clustering/PCA). Note that both
vst return log2 scaled counts, check the manuals and vignettes.
In order to check normalization efficiency I would also not use Z-scored heatmaps. They are rather uninformative on that matter. Instead use MA-plots (e.g. via the
smoothScatter function in
R to get areas colored by density or
LSD) and then check if the bulk of the data centers somewhat along y=0.
modified 16 months ago
16 months ago by
ATpoint ♦ 44k