Dear Norman / all, Many thanks for making PLGEM available to the community. It's a very straightforward package to use. I'm using this for spectral counts measured on four conditions. As far as I understand, PLGEM can fit the model to one comparison of interest at a time (Control_vs_condirion1, control_vs_condition2, ...) to detect differential expression. . I'm wondering if there is anyway to calculate p-values for multi-group comparison? Basically what I'm after is to make all pair-wise comparisons to detect the significance of changes over all conditions. The same as what limma does with f-statistics. Many thanks in advance, Amin On Sun, Oct 24, 2010 at 6:34 PM, Pavelka, Norman <nxp@stowers.org> wrote: > Hi Yolande, > > The error message is telling you that there is no condition called 'C' in > your ExpressionSet. In fact, if you look at your 'pData' you only have two > conditions, either condition 'M' or 'F'. Try running it again changing the > value of argument 'fitCondition' to either 'M' or 'F'. > > On a separate note, if the only thing you want to change compared to the > default behaviour is the significance level 'delta', you don't have to use > the step-by-step mode. You can use the wrapper mode, and simply change the > value of argument 'signLev'. > > Let me know how it works. I'll be happy to help more. > BTW, if you reply through the Bioconductor mailing list, also others can > benefit from the discussion! ;-) > > Thanks! > Norman > > > -----Original Message----- > From: Yolande Tra [mailto:yolande.tra@gmail.com] > Sent: Saturday, October 23, 2010 1:19 PM > To: Pavelka, Norman > Subject: Re: [BioC] limma for spectral counts > > Hi Norman, > > Thank you for your reply. I tried the method using the step-by-step mode, > since I want to use delta = 0.05 (not 0.001) but it did not work. Here is > all the code I run. I built an expressionset for the data using pData1 > (attached file). I have 4 replicates for condition C and 5 replicates for > PLS. I used the same notation as in the tutorial. > > library(plgem) > library("Biobase") > exprs <- as.matrix(read.table("phtn102210.txt", header = TRUE, sep = "\t", > row.names = 1, as.is = TRUE)) pData <- read.table('pData1.txt', row.names > = 1, header = TRUE, sep = "\t") > rownames(pData) > all(rownames(pData) == colnames(exprs)) phenoData <- > new("AnnotatedDataFrame", data = pData) exampleSet <- new("ExpressionSet", > exprs = exprs, phenoData = phenoData) > > exampleSet > ExpressionSet (storageMode: lockedEnvironment) > assayData: 865 features, 9 samples > element names: exprs > protocolData: none > phenoData > sampleNames: C1, C2, ..., LPS5 (9 total) > varLabels and varMetadata description: > conditionName: NA > featureData: none > experimentData: use 'experimentData(object)' > Annotation: > > > phenoData(exampleSet) > An object of class "AnnotatedDataFrame" > sampleNames: C1, C2, ..., LPS5 (9 total) > varLabels and varMetadata description: > conditionName: NA > > It seems that the same description is outputed for your data LPSeset and my > data exampleSet, but still gave me an error. > > LPSfit <- plgem.fit(data = exampleSet, covariate = 1, fitCondition = "C", p > = 10, q = 0.5, plot.file = FALSE, fittingEval = TRUE, verbose = > TRUE) > Error in .checkCondition(fitCondition, "fitCondition", covariate, > pData(data)) : condition 'C' is not defined in the input ExpressionSet for > function 'plgem.fit'. > > Thank you for your help, > Yolande > > On Fri, Oct 22, 2010 at 7:49 PM, Pavelka, Norman <nxp@stowers.org> wrote: > > Hi Yolande, > > > > You can try normalizing your specral counts following the NSAF > (Normalized Spectral Abundance Factor) approach and then you can use package > 'plgem' to detect your differentially abundant proteins. You can have a look > at this publication to get an idea and then let me know if you need any > help: > > > > http://www.ncbi.nlm.nih.gov/pubmed/18029349 > > > > Thanks and good luck! > > Norman > > > > > > On 20 October 2010 14:20, Yolande Tra <yolande.tra@gmail.com> wrote: > >> Hello list members, > >> > >> I was wondering if limma method can be used for spectral counts of > >> proteins from mass spectrometry. If yes, is there a function in > >> Bioconductor that normalizes these counts.before running limma. > >> > >> Thank you for your help, > >> > >> Yolande > >> > >> _______________________________________________ > >> Bioconductor mailing list > >> Bioconductor@stat.math.ethz.ch > >> https://stat.ethz.ch/mailman/listinfo/bioconductor > >> Search the archives: > >> http://news.gmane.org/gmane.science.biology.informatics.conductor > >> > > > > Norman Pavelka, Ph.D. > > Postdoctoral Research Associate > > Rong Li lab > > Stowers Institute for Medical Research 1000 E. 50th St. > > Kansas City, MO 64110 > > U.S.A. > > > > phone: +1 (816) 926-4103 > > fax: +1 (816) 926-4658 > > e-mail: nxp@stowers.org > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]