Hi Rory,



I ran DiffBind using DEseq2 to get list of differential peaks between tumors and normals (3 biological replicates each). At the same time, I extracted the raw read count matrix from DiffBind and ran DESeq2 independently. However I get different results. To explain further -



DiffBind-



> h3k4me3_readin <- dba(sampleSheet="h3k4me3.csv") ## Read in datasheet

> h3k4me3_counts <- dba.count(h3k4me3_readin, peaks=h3k4me3_peakset, score=DBA_SCORE_READS)

> h3k4me3_contrast = dba.contrast(h3k4me3_counts, categories=DBA_CONDITION, block=DBA_REPLICATE)

> h3k4me3_deseq2_analysis <- dba.analyze(h3k4me3_contrast, method= DBA_DESEQ2, bReduceObjects=FALSE, bSubControl=FALSE)



On applying a filter of 0.05 FDR - I got 1261 DE peaks.



DESeq2-



## CountData - Dataframe created from CSV file extracted from h3k4me3_counts

## PhenoData - Attached group/condition information in a dataframe



> dds <- DESeqDataSetFromMatrix(countData = CountData, colData = phenodata, design = ~ Replicate + Condition)

> dds2 <- DESeq(dds)

> res <- results(dds2)



On applying a filter of 0.05 on the adjusted p value in 'res' dataframe, I got 743 DE peaks.



Could you please explain why I see this difference ?



Thanks !

Aditi







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