[BioC] limma for spectral counts

Pavelka, Norman NXP at stowers.org
Sun Oct 24 18:34:13 CEST 2010


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 at 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
>



More information about the Bioconductor mailing list