20 months ago by
CRUK, Cambridge, UK
DiffBind's perspective, the main difference between
DESeq2 relates to the way they normalise the data.
edgeR's TMM normalization method in a way that assumes that most of the sites are not differentially bound. If this assumption is violated -- for example, we have experiments where we knock down the factor we are ChIPing -- the result as normalized using
method=DBA_EDGER can be (quite) incorrect. It is for this reason that in the development version of
DiffBind, the default has been changed to
method=DBA_DESEQ2, at least until we can add some more advanced normalization features.
In your case, if one of your conditions is hypo-methylated and the other hyper-methylated, the normalization can be doing the wrong thing. It is worth looking to see if this is what is going on. One way to do this is to use
dba.plotMA() to make three plots, and compare them:
> dba.plotMA(myDBA, method=DBA_EDGER, bNormalized=FALSE)
> dba.plotMA(myDBA, method=DBA_EDGER, bNormalized=TRUE)
> dba.plotMA(myDBA, method=DBA_DESEQ2, bNormalized=TRUE)
Comparing the non-normalized data to the version normalized using
DESeq2 may shed light on what is going on.
Besides that, a ~15% difference is actually pretty decent agreement. After all, the threshold is somewhat arbitrary (the default is changing from 0.10 to 0.05 in future releases). It may be interesting to see how well the different methods agree on the specific sites they identify. You can get an idea of how consistent things are by plotting a Venn diagram:
> dbsites <- dba.plotVenn(myDBA,contrast=1,method=DBA_ALL_METHODS)
Note that the three peaksets in the Venn diagram (peaks identified by only
DESeq2,and both methods) are returned in