You can get the binding matrix after the call to dba.count, but you should make sure to set the "score" to either DBA_SCORE_READS or DBA_SCORE_READS_MINUS. If you've already counted and don't want to do it again, you can change the score and then retrieve the binding matrix: > DBA = dba.count(DBA,peaks=NULL,score=DBA_SCORE_READS) > bindingMatrix = dba.peakset(DBA, bRetrieve=TRUE) This will return the binding matrix as a GRanges object, with the read counts in the metadata. It may be easier to work with a dataframe: > bindingMatrix = dba.peakset(DBA, bRetrieve=TRUE, DataType=DBA_DATA_FRAME) In which case the count matrix is: > counts = bindingMatrix[,4:ncol(bindingMatrix)] As for retrieving the actual edgeR and DESeq object, the final section int he vignette describes where they are after a call to dba.analyze(). Cheers- Rory On 09/09/2014 15:29, "Enrico Ferrero" <enricoferrero86 at="" gmail.com=""> wrote: >Hi Rory, > >Many thanks for the explanation. At first sight, using a blocking >factor should probably do the job - I hadn't though about it. >For future reference, how and at what step I can extract the binding >matrix or edgeR/DESeq2 objects to continue the analysis with those >packages? > >Thanks! >Best, > > >On 9 September 2014 14:43, Rory Stark <rory.stark at="" cruk.cam.ac.uk=""> wrote: >> Hello Enrico- >> >> You can do some more advanced modelling using DiffBind, but to really >>get >> the full power of the GLMs, you probably want to extract the binding >> matrix and/or the edgeR/DESEq2 objects and run the appropriate RNA- seq >> package directly. >> >> Within DiffBind, you can use the "block" parameter in dba.contrast to >> indicate the metadata field that has the timepoint. So if the >> sample/control distinction is indicated as the Treatment and the >>timepoint >> info is in the Condition, you can say: >> >>> DBA = dba.contrast(DBA,categories=DBA_TREATMENT, block=DBA_CONDITION) >>> DBA = dba.analyze(DBA) # for default edgeR analysis >> >> This will model the data as [~Condition + Treatment] and give you the >> effects of the treatment consistent across timepoints. There are other >> models you may want to fit, (eg [~Condition * Treatment]); for this you >> would need to run edgeR (or DESeq/DESeq2) independently -- their >> respective vignettes give examples of analyzing time series data. >> >> -Rory >> >> On 09/08/2014 12:02, Enrico Ferrero <enricoferrero86 at="" gmail.com=""> wrote: >> >>> >>>------------------------------------------------------------------- --- >>> >>>Message: 1 >>>Date: Mon, 8 Sep 2014 12:01:57 +0100 >>>From: Enrico Ferrero <enricoferrero86 at="" gmail.com=""> >>>To: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> >>>Subject: [BioC] DiffBind time course >>>Message-ID: >>> >>><cao22hxcaqm_61p7uh4kskkm13yfn5g5hp7fzs32+cbgtnnpzdw at="" mail.gmail.com=""> >>>Content-Type: text/plain; charset=UTF-8 >>> >>>Hi, >>> >>>Is there a way to use DiffBind to analyse time course data? >>>I have sample and control replicates at five different time points and >>>I would like to know which sites show differential binding over time. >>> >>>At the moment I'm doing multiple pairwise comparisons (i.e: sample at >>>24h vs control at 24h) and I'm trying to understand if it's possible >>>at all and, if yes, what parameters I should pass to dba.contrast() >>>and dba.analyze(). >>> >>>Thanks! >>> >>>-- >>>Enrico Ferrero >> > > > >-- >Enrico Ferrero