Hi Rory Your advice is very useful. But now I want to use the consensus bed file generated by WT group and KO group respectively for the next step of analysis. I will footprinting analysis using the rgt-hint. He asked me to generate a bed file with an integer score for the fifth column. I wonder whether diffbind can extract the two consensus bed files. And the fifth column is an integer. now i had run the code below

dbObj_consensus<-dba(DBA = dbObj_consensus, mask = dbObj_consensus$masks$Consensus,minOverlap = 1)
dbObj_consensus

i get the output

2 Samples, 66064 sites in matrix:
  ID Tissue Factor Replicate Intervals
1 KO Spleen     KO   1-2-3-4     63479
2 WT Spleen     WT   1-2-3-4     57950

I want to get KO's consensus bed which contain 63479 lines and WT's consensus bed which contain 57950 lines Thank you

Thanks for your help. I'm sorry, I have another question: I'm using diffbind to analyze my ATACseq data. When I use a old version(R base is 3.6.2, diffbind is 3.5). my code is

m1 <- "KO"
m2 <- "WT"
dbObj <- dba.count(dbObj, bUseSummarizeOverlaps=TRUE, minOverlap = 0.5)
contr <- paste0(m1, "vs", m2)
dbObj <- dba.contrast(dbObj, group1 = dbObj$masks[[m1]], group2 = dbObj$masks[[m2]])
dbObj <- dba.analyze(dbObj, bFullLibrarySize = FALSE)
dba.show(dbObj, bContrasts=TRUE)

i can get 2500 differential binding site

but when i use R4.2.2 and diffbind is 3.8.0 because this version delete the parameter bFullLibrarySize, my code is :

m1 <- "KO"
m2 <- "WT"
bObj <- dba.count(dbObj, bUseSummarizeOverlaps=TRUE,minOverlap = 0.5,bParallel = 8)
dbObj <- dba.normalize(DBA = dbObj, library = "RiP")
contr <- paste0(m1, "vs", m2)
dbObj <- dba.contrast(dbObj, group1 = dbObj$masks[[m1]], group2 = dbObj$masks[[m2]])
dbObj <- dba.analyze(dbObj)
dba.show(dbObj, bContrasts=TRUE)

I just get 552 differential differential binding site.

I have seen you reply about bFullLibrarySize. like your reply 1 and your reply 2. you said that If bFullLibrarySize=FALSE, the number of reads that overlap consensus peaks is used for each sample (basically, the sum of all the counts). So I used dbObj <- dba.normalize(DBA = dbObj, library = "RiP") to make sure that the method of normalize is consistent.

I known you don't recommand using bFullLibrarySize = FALSE. But the reason I use it is that it filters out more significant peaks at FDR<0.05, which helps me not miss any information that might be useful.

My question is how can I reproduce the results obtained from the old version diffbind ? Thank you!