[BioC] Diffbind: Binding Affinity Heatmap
giuseppe.gallone at dpag.ox.ac.uk
Thu Aug 15 15:17:29 CEST 2013
thanks. Yes that's exactly what happens.
On 08/15/13 13:56, Rory Stark wrote:
> Hi Giuseppe-
> Two compare different peak callers on the same replicate, you can get the
> clustering/correlation at the peak level but it doesn't make sense at the
> count level, as all the peaks are merged into a single consensus set at
> that point.
> You did this correctly in the first case by including a line for each peak
> caller with the same read (bam) files. At that point you can get a
> correlation heatmap, PCA plot, etc, as well as look at overlaps (e.g. by
> using dba.plotVenn and/or dba.overlap).
> One you create a binding matrix, as it done when you run dba.count, you
> are using a single "consensus" set of peaks for all the samples, and
> getting the number of reads in these peaks for each sample. So it no
> longer makes sense to have a different sets of counts for each original
> peakset. This is a result of the peaks being "merged" (by default, all the
> peaks that appear in at least two peaksets are merged into a single set of
> peaks for the rest of the analysis).
> If try what you suggest, and use symbolic links, you should get exactly
> the same result for each virtual replicate -- that is, the three entries
> should have correlation values of 1.0, as the same reads are being counted
> within the same (global, merged) consensus peakset.
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