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
> >
>
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