Binbin, In the current implementation of makeVennDiagram, the time used to calculate p-value does not depend on the totalTest. Noah, thanks so much for sharing your insights! Best regards, Julie On 11/18/10 11:26 AM, "Binbin Liu" <b.b.liu at="" leeds.ac.uk=""> wrote: > Dear Noah, > > Many thanks for your detailed explanation on how totalTest is defined. What I > am doing is similar to the second case. However, the TF we are interested > could bind anywhere on the genome. So with mm9 of 2.7E+9 and peak width <=200 > bps , the totalTest is 1.35E+7. It seems very computational costly to run > ChIPpeakAnno. Nevertheless, do you think it is reasonable? > > > Thanks, > > Binbin > > > On 16 Nov 2010, at 18:41, Noah Dowell wrote: > >> Hello Binbin, >> >> It would be helpful to describe your problem and post to the whole message >> board. (There are many experts who probably can be more helpful than myself >> :-)) That said, I think you are referring to the "NaN" error and below are >> my thoughts (Julie Zhu also answered this a couple of times and her reply is >> probably in the archives). >> >> >> When calling the makeVennDiagram function you want to set the totalTest >> number to something that is larger than the experimentally determined peak >> number. As far as I know, the totalTest number is used for the >> hypergeometric sampling that is used to determine if the overlap between two >> datasets is more than would be expected by chance. So one way to sort this >> out using biological information is to think about the maximum number of >> possible binding events and use that as the totalTest number. For example, >> if you are studying a sequence-specific DNA binding protein with a known >> motif you could count that number of times that motif occurs in the genome >> and compare that to the number of peaks you have experimentally determined. >> >> Motifs = 500 >> Peaks = 200 >> Peaks w/ motif = 180 (90%) >> "upper limit" = 500 >> new "upper limit" for totalTest = .9 x 500 = 450 >> >> Now if your working with a sequence-independent binding factor it can get >> tricky. One approach would be to determine the mean peak width. Then divide >> the whole genome sequence by this number to get an upper limit. This is >> probably way to high so using additional information such as if the protein >> binds intergenic or ORFs could bring the number down but make it more >> relevant to the biological experiment. For example: >> >> peaks = 75 >> intergenic peaks = 70 >> ORF peaks = 5 >> mean peak width = 50 base pairs >> genome size = 10000 base pairs >> "upper limit" = 10000/ 50 = 200 (possible peaks) >> intergenic seq = 4000 base pairs >> new "upper limit" = 4000/50 = 80 (possible intergenic peaks) >> >> I was working with something more like the second case and I felt the >> totalTest based on the total genome was quite relaxed and based on the >> intergenic sequence only was quite stringent so somewhere in the middle might >> be better but most importantly I feel I am standing on some solid biological >> reasoning for determining the amount of sampling. >> >> Hope this helps and I would be interested to here if anybody has some >> critiques of this approach or additional suggestions. >> >> Best, >> >> Noah >> >> >> >> On Nov 16, 2010, at 7:31 AM, Binbin Liu wrote: >> >>> Dear Noah, >>> >>> I saw your post on bioconductor mailing list regarding the totalTest number >>> for the P-val calculation in ChipPeakAnno :: makeVennDiagram(). I am having >>> the same problem. Can I ask how you got it sorted? >>> >>> >>> Many thanks. >>> >>> Binbin >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >