Question: limma for spectral counts
1
gravatar for Yolande Tra
9.1 years ago by
Yolande Tra120
Yolande Tra120 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
limma • 1.1k views
ADD COMMENTlink modified 9.1 years ago by Pavelka, Norman70 • written 9.1 years ago by Yolande Tra120
Answer: limma for spectral counts
3
gravatar for Naomi Altman
9.1 years ago by
Naomi Altman6.0k
Naomi Altman6.0k wrote:
Dear Yolande, I do not know anything about spectral count data. However, limma is meant for continuously distributed data. If the counts are large, then limma can be used on log(count). However, if the counts are small, then the methods in edgeR and DEseq are more suitable as they use a typical assumption for count data - Negative Binomial distribution. --Naomi At 11:23 AM 10/20/2010, Laurent Gatto wrote: >Dear Yolande, > >The spectral counts should indeed be normalised but, as far as I know, >there is no direct way to do this in Bioconductor. It should however >not be too difficult to implement if you have the sequence to >normalise the count to the length of the protein. You might also want >to have a look at the emPAI [1] to assess protein abundance from >spectral counts. The emPAI values will probably need some log >transformation before using limma. > >If you want to use normalised spectral counts, another option would be >to investigate the use of RNA-seq methods that are meant to work with >counts. edgeR mentions spectral counts in the publication [2], but I'm >sure other Bioconductor packages can equally apply (see for instance >the RNAseq Bioc view). > >Hope this helps. > >Best wishes, > >Laurent > >[1] http://bioinformatics.oxfordjournals.org/content/26/4/576.abstract >[2] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/ >[3] http://www.bioconductor.org/help/bioc- views/2.7/BiocViews.html#___RNAseq > > >-- >Laurent Gatto >Cambridge Centre For Proteomics >http://www.bio.cam.ac.uk/proteomics > >On 20 October 2010 14:20, Yolande Tra <yolande.tra at="" 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 at 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 at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD COMMENTlink written 9.1 years ago by Naomi Altman6.0k
Answer: limma for spectral counts
0
gravatar for Laurent Gatto
9.1 years ago by
Laurent Gatto1.2k
Belgium
Laurent Gatto1.2k wrote:
Dear Yolande, The spectral counts should indeed be normalised but, as far as I know, there is no direct way to do this in Bioconductor. It should however not be too difficult to implement if you have the sequence to normalise the count to the length of the protein. You might also want to have a look at the emPAI [1] to assess protein abundance from spectral counts. The emPAI values will probably need some log transformation before using limma. If you want to use normalised spectral counts, another option would be to investigate the use of RNA-seq methods that are meant to work with counts. edgeR mentions spectral counts in the publication [2], but I'm sure other Bioconductor packages can equally apply (see for instance the RNAseq Bioc view). Hope this helps. Best wishes, Laurent [1] http://bioinformatics.oxfordjournals.org/content/26/4/576.abstract [2] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/ [3] http://www.bioconductor.org/help/bioc- views/2.7/BiocViews.html#___RNAseq -- Laurent Gatto Cambridge Centre For Proteomics http://www.bio.cam.ac.uk/proteomics On 20 October 2010 14:20, Yolande Tra <yolande.tra at="" 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 at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >
ADD COMMENTlink written 9.1 years ago by Laurent Gatto1.2k
Answer: limma for spectral counts
0
gravatar for Pavelka, Norman
9.1 years ago by
Pavelka, Norman70 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 at="" 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 at 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 at stowers.org
ADD COMMENTlink written 9.1 years ago by Pavelka, Norman70
Answer: limma for spectral counts
0
gravatar for Pavelka, Norman
9.1 years ago by
Pavelka, Norman70 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 at="" 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 at="" 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 at 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 at stowers.org > > _______________________________________________ > 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 >
ADD COMMENTlink written 9.1 years ago by Pavelka, Norman70
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 346 users visited in the last hour