Hi, Thanks for the fast reply, Just another curiosity, I finally obtained list of genes significantly differentially expressed between my conditions, I would like now to build for each condition an heatmap. I constructed for each one a data matrix based on a lumi.N.Q object (after VST variance stabilizing transformation and normalization). >dataMatrix <- exprs(lumi.N.Q) >head(dataMatrix) CMe_2 mES_2 CMa_1 CMp_1 CMe_1 mES_1 CMa_3 CMp_3 CMe_3 mES_3 CMa_2 CMp_2 T.kvUVfFyruaCeqZVs 7.306945 7.312214 7.302783 7.302670 7.294789 7.273550 7.381502 7.312916 7.301191 7.268140 7.272041 7.278205 Q96MoiqM_z49FU37tE 7.257576 7.278205 7.300322 7.226146 7.281574 7.281210 7.294743 7.393729 7.340853 7.315099 7.312916 7.270886 >heatmap(dataMatrix[1:5,]) I was wondering on the scale of the heatmap, usually values ranges from -3 to 3, but data VST transformed in my example ranges from 7 to 14, with a median value of 7.5... is it correct to plot a heatmap with VST transfmed data? or is preferable to perfrom variance stabilization using log2 transform? If is correct to use vst transformed data how do I refer to the scale? Thanks for any suggestion, Cheers, Paolo 2012/8/23 Pan Du <dupan.mail at="" gmail.com="">: > Hi Paolo > > It's pretty normal that some probes cannot map to any genes because > Human genome annotation keeps updating but the probe design is > unchanged. So usually you can ignore those NA probes. If you are > really interested in them, you can easily convert the nuID to probe > sequence (use id2seq function) and map them to genome or refseq. > I will update the vignette later to avoid such confusion. > > Pan > > On Thu, Aug 23, 2012 at 3:26 AM, Paolo Kunderfranco > <paolo.kunderfranco at="" gmail.com=""> wrote: >> Dear All, >> I am working with lumi / limma package to detect differentially expressed >> genes between two or more samples. >> I was wondering why when I add geneSymbol and geneName to my Illumina >> probelist most of them (around 10%)are not called and remained NA, for >> instance (last row): >> >> >> if (require(lumiMouseAll.db) & require(annotate)) { >> geneSymbol <- getSYMBOL(probeList, 'lumiMouseAll.db') >> geneName <- sapply(lookUp(probeList, 'lumiMouseAll.db', >> 'GENENAME'), function(x) x[1]) >> fit1_2$genes <- data.frame(ID= probeList, >> geneSymbol=geneSymbol, geneName=geneName, stringsAsFactors=FALSE) >> } >> >> >> 7671 69fpKOOuFduFbAjNVU Dppa5a developmental pluripotency >> associated 5A 7.828381 9.333743 149.31710 2.773571e-18 6.144846e-14 >> 29.80858 >> 16014 QpWgiAmByT4gW7iui0 Pou5f1 POU domain, class 5, transcription >> factor 1 5.305532 8.633706 103.85793 1.098423e-16 8.143726e-13 >> 27.72832 >> 20450 HlUzpCHheswfSZNdQo Trh thyrotropin >> releasing hormone 5.603441 8.761965 103.81774 1.102739e-16 >> 8.143726e-13 27.72571 >> 7670 o7Ah_nzF7JdZOTtd9U Dppa4 developmental pluripotency >> associated 4 5.300619 8.626239 99.82457 1.640729e-16 9.087587e-13 >> 27.45790 >> 7672 xjn0tTp4isUXmUkAKI Dppa5a developmental pluripotency >> associated 5A 7.663922 9.439668 97.09091 2.173661e-16 9.631491e-13 >> 27.26346 >> 17719 ZXvxHuC6s3xogRFJfo Sall4 sal-like 4 >> (Drosophila) 4.456642 8.585243 90.39110 4.484584e-16 1.655932e-12 >> 26.74469 >> 14084 06jqfFxe5_X97NRXuk Myl3 myosin, light >> polypeptide 3 -7.736059 13.128014 -88.39591 5.622067e-16 1.779384e-12 >> 26.57755 >> 8757 oii7mSFyrr_AMWODH0 <na> >> <na> 4.608167 8.438770 78.65631 1.833459e-15 5.077535e-12 >> 25.66512 >> >> any ideas? >> thanks >> paolo >>