Hi,

I have several samples divided into 5 conditions, such as A, B, C, D, E. All samples are from different tissues, no replicate. I want to differentiate them according to 5 conditions by DiffBind package, and display result on heatmap. So I did as follows:

>ALL <- dba(sampleSheet="mysamples.csv")

>ALL_count <- dba.count(ALL, summits=250)

>ALL_contrast <- dba.contrast(ALL_count, categories=DBA_CONDITION)

>ALL_analyze <- dba.analyze(ALL_contrast)

>plot(ALL_analyze)

But the heatmap do not show as the same as I expected, for example, a sample of A should belong to A, but it is mixed with sample of B on the heatmap.

Under the premise of not deleting the sample as much as possible, I want to ask how could I set up parameters to clearly differentiate samples by conditions? Or which parameter I could use for accomplishing my expectation?

Thanks for answering!

Best,

Meng

As you are not specifying a contrast when calling the plot() function (really dba.plotHeatmap()), it is always plotting using the global binding matrix. In order to get plots based on the differentially bound sites, you need to add contrast=n to the call, where n is the number of the comparison. You can see the contrast numbers by printing out the DBA object ALL_analyze. Programmatically, you can get the contrast numbers by calling:

> contrasts <- dba.show(ALL_analyze, bContrasts=TRUE)

It is worth noting that even after running a differential analysis, and plotting only the differentially bound sites, you may see some samples clustering in ways other than you would expect. It can be useful to use PCA plots (dba.plotPCA()) to get more insight into how the samples are clustering, or to lower the FDR threshold to include only the differential sights with the highest confidence.