[BioC] How to extract expressed genes from Affymetrix data

[Belinda Phipson]

Hi Michelle

The function propexpr() in the limma package will estimate the proportion of
expressed probes in each array, as long as there are some negative controls
present in your data.

You can also estimate the number of probes that are not differentially
expressed between WT and knock-out using the convest() function in limma. It
takes a vector of p-values. The proportion of differentially expressed genes
will be 1-convest(p-vals).

Cheers,
Belinda

-----Original Message-----
From: bioconductor-bounces at r-project.org
[mailto:bioconductor-bounces at r-project.org] On Behalf Of michelle_low
Sent: Monday, 23 July 2012 3:56 PM
To: bioconductor at r-project.org
Subject: [BioC] How to extract expressed genes from Affymetrix data

Hi all,
 
I have done the differential expression analysis below but I wanna know the
total number of expressed genes/transcripts in each cell (wild type and the
mir223 knockout cell). Can somebody help? Thanks in advance..
 
 
Regards,
Michelle
 
 
 
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> library(affy)
Loading required package: Biobase

Welcome to Bioconductor

  Vignettes contain introductory material. To view, type
  'browseVignettes()'. To cite Bioconductor, see
  'citation("Biobase")' and for packages 'citation("pkgname")'.

>  library(limma)
>
pd=read.AnnotatedDataFrame("phenodata.txt",header=TRUE,sep="",row.names=1)
> a=ReadAffy(filenames=rownames(pData(pd)),phenoData=pd,verbose=TRUE)
1 reading wildrep1.CEL ...instantiating an AffyBatch (intensity a 1004004x6
matrix)...done.
Reading in : wildrep1.CEL
Reading in : wildrep2.CEL
Reading in : wildrep3.CEL
Reading in : miR223rep1.CEL
Reading in : miR223rep2.CEL
Reading in : miR223rep3.CEL
>  x=rma(a)
Background correcting
Normalizing
Calculating Expression
> c=paste(pd$treatment,pd$n,sep="")
> f=factor(c)
> design=model.matrix(~0+f)
>  colnames(design)=levels(f)
> fit=lmFit(x,design)
> library(mouse4302.db)
Loading required package: AnnotationDbi
Loading required package: org.Mm.eg.db
Loading required package: DBI


> fit$genes$Symbol <- getSYMBOL(fit$genes$ID,"mouse4302.db")
Error: could not find function "getSYMBOL"
> library(annotate)
> fit$genes$Symbol <- getSYMBOL(fit$genes$ID,"mouse4302.db")
> contrast.matrix=makeContrasts(E="present-absent")
Error in .Internal(inherits(x, what, which)) : 'x' is missing
> contrast.matrix=makeContrasts(E="present-absent",levels=design)
> fit2=contrasts.fit(fit,contrast.matrix)
> fit2=eBayes(fit2)
> results1 <-topTable (fit2, coef=1, p.value=0.3,number=nrow(fit2))
> dim(results1)
[1] 889   8
> results1 <-topTable (fit2, coef=1, p.value=0.5,number=nrow(fit2))
> dim(results1)
[1] 2997    8
> results1 <-topTable (fit2, coef=2, p.value=0.5,number=nrow(fit2))
Error in toptable(fit = fit[c("coefficients", "stdev.unscaled")], coef =
coef,  : 
  subscript out of bounds
> write.table(results1, file="WT-KO.txt")
> results1 <-topTable (fit2, coef=2, p.value=0.4,number=nrow(fit2))
Error in toptable(fit = fit[c("coefficients", "stdev.unscaled")], coef =
coef,  : 
  subscript out of bounds
> results1 <-topTable (fit2, coef=1, p.value=0.4,number=nrow(fit2))
> dim(results1)
[1] 1668    8
> write.table(results1, file="WT-KO.txt")

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