Hi all, Trying to use limma(code below) to do some DE analysis...and I get negative p-values. Not quite sure where I went wrong here...please help! Thanks. Avoks. gds158dat = getGEO('GDS158',destdir=".") gds158eset = GDS2eSet(gds158dat, do.log2=TRUE) groups= pData(gds158eset)$metabolism groups=as.character(groups) groups[groups=="insulin sensitive"]= "IS" groups[groups=="insulin resistant"]= "IR" f = factor(groups, levels=c("IS","IR")) design_gds158 = model.matrix(~0+f) colnames(design_gds158) = levels(f) cont.matrix = makeContrasts(IR-IS, levels=design_gds158) fit=lmFit(gds158eset, design_gds158) fit2 = contrasts.fit(fit, cont.matrix) fit2 = eBayes(fit2) results = topTable(fit2, adjust ="BH", number = nrow(gds158eset)) sessionInfo() R version 2.14.1 (2011-12-22) Platform: i386-pc-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=English_xxx.1252 LC_CTYPE=English_xxx.1252 [3] LC_MONETARY=xxx.1252 LC_NUMERIC=C [5] LC_TIME=xxx.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] ArrayExpress_1.14.0 GEOquery_2.20.8 limma_3.10.2 [4] XML_3.9-4.1 RCurl_1.91-1.1 bitops_1.0-4.1 [7] puma_2.6.0 mclust_3.4.11 affy_1.32.1 [10] Biobase_2.14.0 loaded via a namespace (and not attached): [1] affyio_1.22.0 BiocInstaller_1.2.1 preprocessCore_1.16.0 [4] tools_2.14.1 zlibbioc_1.0.0

I have posted this several times, but it has been a while so I will do it again. Suppose p=.001 and you have 10000 genes. Then you expect 10 significant genes just by chance. So if you observe 9 or 10 significant genes at this level, your FDR should be 100%. If you observe 11, your FDR should be about 10/11=0.91. Etc. This does not mean that you don't have any significant genes. It means your experiment did not have enough power to detect them. --Naomi At 10:31 AM 3/15/2012, Ovokeraye Achinike-Oduaran wrote: >Hi, > >Excuse the language but I say shady because they all range from >~0.918 - 0.999. > >On Thu, Mar 15, 2012 at 3:54 PM, Steve Lianoglou ><mailinglist.honeypot at="" gmail.com=""> wrote: > > Hi, > > > > On Thu, Mar 15, 2012 at 9:50 AM, Ovokeraye Achinike-Oduaran > > <ovokeraye at="" gmail.com=""> wrote: > >> Hi all, > >> > >> Sorry...a recall of my previous mail...the p-values are fine(or not) > >> but not negative...the corrected p-values, however, look very shady. > >> Any suggestions there? > > > > Shady? > > > > Need to elaborate, please. > > > > -steve > > -- > > Steve Lianoglou > > Graduate Student: Computational Systems Biology > > | Memorial Sloan-Kettering Cancer Center > > | Weill Medical College of Cornell University > > Contact Info: http://cbio.mskcc.org/~lianos/contact > >_______________________________________________ >Bioconductor mailing list >Bioconductor at r-project.org >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor

Hi all, Sorry...a recall of my previous mail...the p-values are fine(or not) but not negative...the corrected p-values, however, look very shady. Any suggestions there? Thanks On Thu, Mar 15, 2012 at 3:46 PM, Ovokeraye Achinike-Oduaran <ovokeraye at="" gmail.com=""> wrote: > Hi all, > > Trying to use limma(code below) to do some DE analysis...and I get > negative p-values. Not quite sure where I went wrong here...please > help! > > Thanks. > > Avoks. > > gds158dat = getGEO('GDS158',destdir=".") > gds158eset = GDS2eSet(gds158dat, do.log2=TRUE) > groups= pData(gds158eset)$metabolism > groups=as.character(groups) > groups[groups=="insulin sensitive"]= "IS" > groups[groups=="insulin resistant"]= "IR" > f = factor(groups, levels=c("IS","IR")) > design_gds158 = model.matrix(~0+f) > colnames(design_gds158) = levels(f) > cont.matrix = makeContrasts(IR-IS, levels=design_gds158) > fit=lmFit(gds158eset, design_gds158) > fit2 = contrasts.fit(fit, cont.matrix) > fit2 = eBayes(fit2) > results = topTable(fit2, adjust ="BH", number = nrow(gds158eset)) > > sessionInfo() > R version 2.14.1 (2011-12-22) > Platform: i386-pc-mingw32/i386 (32-bit) > > locale: > [1] LC_COLLATE=English_xxx.1252 ?LC_CTYPE=English_xxx.1252 > [3] LC_MONETARY=xxx.1252 LC_NUMERIC=C > [5] LC_TIME=xxx.1252 > > attached base packages: > [1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base > > other attached packages: > ?[1] ArrayExpress_1.14.0 GEOquery_2.20.8 ? ? limma_3.10.2 > ?[4] XML_3.9-4.1 ? ? ? ? RCurl_1.91-1.1 ? ? ?bitops_1.0-4.1 > ?[7] puma_2.6.0 ? ? ? ? ?mclust_3.4.11 ? ? ? affy_1.32.1 > [10] Biobase_2.14.0 > > loaded via a namespace (and not attached): > [1] affyio_1.22.0 ? ? ? ? BiocInstaller_1.2.1 ? preprocessCore_1.16.0 > [4] tools_2.14.1 ? ? ? ? ?zlibbioc_1.0.0