Thank you. Your answer now brought one more question. Let me explain a little better my experiment layout
I have reprogrammed cells with 3 transcription factors (A, B and C) and have 6 experimental conditions:
condition C1: targets of A together with other TF (when cells were transfected with A,B and C)
condition C2: targets of A alone (when cells were transfected only with A)
condition C3: targets of B together with other TF
condition C4: targets of B alone (when cells were transfected only with B)
condition C5: targets of C together with other TF
condition C6: targets of C alone (when cells were transfected only with C)
For all conditions I have a ChIP and Input samples.
For conditions C1 to C4 I have two replicates of ChIP and two replicates of Input. And so, my plan is for each of these conditions to call peaks for replicate 1 (using ChIP and Input) and peaks for replicate 2 (using ChIP and Input) and then do IDR to identify common significant peaks between the two replicates.
For conditions C5 to C6 I only have one sample (no replicates) for ChIp and Input. So, I'll select significant peaks based on FDR.
Then I want to do a differential binding analysis (with MAnorm, DIME or DBChIP) between conditions C1 vs C2, C3 vs C4, C5 vs C6 to identify the targets that are more differentially bind by each TF when is together with other vs when is alone. With this I would get 3 lists of targets that would be converted into genes.
Finally, I would either intersect or join genes of the three lists.
Is this analysis ok, or does it conflict with what you said of not not being able to quantitatively compare TFs?