Hi Yasmine,
bamCoverage has a few options for normalization, of which usually RPKM
or RPGC (aka 1x normalization) are used. Of those, I prefer RPGC,
since it normalizes the sample to an average 1x coverage. You can also
have it produce an unnormalized track, which is handy in some cases.
Regarding computeMatrix, you're correct that it assumes that your
bigWig files were created in a way to make them comparable in some
way, since it performs no normalization. This is useful, since you
might combine RNA-seq, ChIP-seq and methylation data in the same
matrix and all of those will necessarily need to be on very different
scales. The only sort of normalization that computeMatrix can do is if
you select `scale-regions`, which causes it to normalize regions to
the same length. In short, computeMatrix iterates over the regions in
your BED/GTF file(s) and, for each extracts the signal from your
bigWig files and averages. If requested (e.g., with `scale-regions`),
it will scale the length of these regions so that they're all the same
length (e.g., by interpolating) and then save the result in a big
compressed text file with per-bin values.
plotHeatmap mostly just plots the result. It can perform some
clustering and such as well.
Devon
--
Devon Ryan, Ph.D.
Email:
dpr...@dpryan.com
Data Manager/Bioinformatician
Max Planck Institute of Immunobiology and Epigenetics
Stübeweg 51
79108 Freiburg
Germany
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