Hi all I am using DiffBind (R/3.4.1, DiffBind_2.4.8) to identify differentially accessible peaks from ATACseq (peaks called using MACS2 --no model, with broad region calling off) of a given cell type for three different conditions which I shall call A (2 samples), B (5 samples) and C (2 samples). I wish to compare ‘condition’ A to B as well as compare B to C. My code is as follows:
B_C<-dba(sampleSheet = " B_C.csv")
B_C <-dba.contrast(B_C,categories = DBA_CONDITION, minMembers = 2)
B_C <-dba.analyze(B_C, method = DBA_ALL_METHODS)
A_B<-dba(sampleSheet = " A_B.csv")
A_B <-dba.contrast(A_B,categories = DBA_CONDITION, minMembers = 2)
A_B <-dba.analyze(A_B, method = DBA_ALL_METHODS)
I have two questions/concerns:
1) The B-C comparison analysis yields sensible results but the results of the A-B comparison analysis seem spurious – the affinity has shifted exclusively in one direction, with all peaks differentially accessible in the condition A compared to B (regardless of whether EdgeR or DESeq2 is used). Even peaks which feature in 4 out of the 5 B samples but neither of the 2 A samples come up as differentially accessible in A compared to B. Do you have any suggestions as to what might be causing this discrepancy and how I can overcome this problem?
2) Is there a way in which to compare differential accessibility between the A condition and those peaks in the B group which are themselves differentially accessible compared to C i.e. A versus B[!C]? I haven’t been able to identify a way in which to do this from the DiffBind manual. It would be possible to take the DiffBind results of B versus C and then implement DiffBind a second time using these results in comparison to condition A which, however, would preclude the use of replicates as part of the second DiffBind run. An alternative solution I have thought about would be to carry out A-B and B-C separately (as outlined in ‘question 1’ above), combine the regions from both of these analyses into a single BED file and then perform clustering e.g. using the k means algorithm to identify clusters in which A is enriched compared to B or vice versa but B is enriched compared to C. Your thoughts would be welcome.
Thanks in advance.