Question: Integrating the computed spike-in coefficients for the normalisation before differential peaks analysis
0
4 weeks ago by

Hi! Have a question about using DiffBind for ChIP-seq data with drosophila spike-ins.

I already have computed peaks from usage of control data and spike-in normalisation coefficients for a cohort of target samples across two conditions. The spike-in coefficients computation is based on drosophila alignment results as described in ActiveMotif documentation for down-sampling (adjustment based on minimum).

Is there an easy way to use these computed coefficients for the DiffBind analysis? I found this post, but there is a link to pipeline which starts from reads, while I would like only to correct the computed scores matrix before differential analysis. The matrix is computed from original samples without subsampling, but with the usage of control.

chipseq diffbind spike-ins • 68 views
modified 24 days ago by Rory Stark3.0k • written 4 weeks ago by Konstantin Okonechnikov20
Answer: Integrating the computed spike-in coefficients for the normalisation before diff
1
24 days ago by
Rory Stark3.0k
CRUK, Cambridge, UK
Rory Stark3.0k wrote:

You could retrieve the computed read counts using dba.peakset(), then correct them and use them as in the reference.

Thanks a lot for the reply! My further question: how to create the object with adjusted coefficients for further processing? Is there a specific way to set the reference? Here's my code example:

# standard object creation
dtCounts <- dba.count(dt)
# retrieve peaks
peaksRes <- dba.peakset(dtCounts,1:numSamples,bRetrieve=TRUE)
# use spike-in coefficients to adjust results, here's example for one sample
peaksRes$sample1 = peaksRes$sample1 * k1
# how to re-write the target analysis object with adjusted values?
# standard analysis continues....