Hi,

I have 2 replicates each of 4 different samples, so 8 samples total (P300 chip-seq).

All are from the same cell type, but with different transcription factors knocked out, and also WT.

I want to get "modules" of peaks gained and/or lost in each sample (enhancers created or repressed by each TF).

How might I set this up in DiffBind? 

Right now I have made my samples object like this:

8 Samples, 30757 sites in matrix:
               ID Tissue Factor    Condition Treatment Replicate Caller Intervals FRiP
1            WT.1   Th2   P300           WT       5d         1 counts     30757 0.18
2            WT.2   Th2   P300           WT       5d         2 counts     30757 0.14
3      Gata3_cKO.1   Th2   P300      Gata3_KO       5d         1 counts     30757 0.19
4      Gata3_cKO.2   Th2   P300      Gata3_KO       5d         2 counts     30757 0.13
5       Stat6_KO.1   Th2   P300      Stat6_KO       5d         1 counts     30757 0.17
6       Stat6_KO.2   Th2   P300      Stat6_KO       5d         2 counts     30757 0.15
7 Gata3_Stat6_dKO.1   Th2   P300 Gata3_Stat6_KO       5d         1 counts     30757 0.14
8 Gata3_Stat6_dKO.2   Th2   P300 Gata3_Stat6_KO       5d         2 counts     30757 0.12

Many thanks for any help

Hi Sarah-

First, a caveat: having only two replicates of each condition is probably not enough to get good results out of this type of analysis.

That said, the most straightforward design would be to calculate the impact of each knock-out separately, as compared to WT. You can add these three contrasts as follows:

> myDBA <- dba.contrast(myDBA, group1=myDBA$masks$Gata3_KO, 
                        group2=myDBA$masks$WT,
                        name1="Gata3_KO", name2="WT")
> myDBA <- dba.contrast(myDBA, group1=myDBA$masks$Stat6_KO, 
                        group2=myDBA$masks$WT,
                        name1="Stat6_KO", name2="WT")
> myDBA <- dba.contrast(myDBA, group1=myDBA$masks$Gata3_Stat6_KO, 
                        group2=myDBA$masks$WT,
                        name1="Gata3_Stat6_KO", name2="WT")

If you are interested in comparing how binding changes between the double knock-out and each of the single knock-outs, you could add the following two contrasts as well:

> myDBA <- dba.contrast(myDBA, group1=myDBA$masks$Gata3_KO,
                        group2=myDBA$masks$Gata3_Stat6_KO,
                        name1="Gata3_KO", name2="Gata3_Stat6_KO")
> myDBA <- dba.contrast(myDBA, group1=myDBA$masks$Stat6_KO,
                        group2=myDBA$masks$Gata3_Stat6_KO,
                        name1="Stat6_KO", name2="Gata3_Stat6_KO")

Then run an analysis:

> myDBA <- dba.analyze(myDBA)

If you want to, say, see how the changed sites overlap between the three knockouts (compared to WT), you can get a Venn diagram as follows:

> dbSites <- dba.report(myDBA, bDB=TRUE)
> dba.plotVenn(dbSites, 1:3)

-Rory