Hi, I'm trying to use DiffBind for my ChIPseq analysis, but I encountered the following "problem".

I loaded two patients' groups of samples using dba function and setting minOverlap equal to 2. Same setting also for:

my_peaks <- dba.count(my_peaks, bUseSummarizeOverlaps=F,minOverlap=2).

I defined the contrast and then I ran a differential analysis: my_peaks <- dba.analyze(my_peaks,method=DBA_DESEQ2)

But when I looked into this object: peaks_DF <- dba.report( my_peaks,bCalled=TRUE,th=0.1) I found some peaks present only in 1 one sample for the groupA, and only in one sample for the groupB.

Is there any possibility to avoid this and having only a minOverlap of 2 in each patients' group? I mean at least a minimum of overlap=2 in each considered category (>=2 in the groupA and >=2 in the group B, not >=2 groupA+groupB) to create a consensus on which perform differential analysis.

Thank you in advance for your help!

You can make the consensus peakset in two steps: first making a consensus for each sample group with minOverlap=2, then taking the union of these with minOverlap=1.

Here's an example using the sample data, where we get the peaks that overlap in at least two of the Responsive samples and at least two of the Resistant samples, the count using all of these peaks:

# Load peak data
data(tamoxifen_peaks)

# Generate consensus for each condition
tamoxifen.consensus <- dba.peakset(tamoxifen,consensus = DBA_CONDITION, minOverlap=2)

# Examine sample groups consensus peaks and overlap
dba.show(tamoxifen.consensus,tamoxifen.consensus$masks$Consensus)
dba.plotVenn(tamoxifen.consensus,tamoxifen.consensus$masks$Consensus)

# Retrieve union of consensus peaks
consensus <- dba.peakset(tamoxifen.consensus, bRetrieve=TRUE,
                         peaks=tamoxifen.consensus$masks$Consensus,
                         minOverlap=1)

# Count using pre-computed consensus peakset
tamoxifen <- dba.count(tamoxifen, peaks=consensus)