Benilton , 

Many thanks for your response, the problem is that I´m trying to get levels of gene expression (but whithout comparing different samples) from arrays and I think it is not a good approach. I´m already posted to Bioconductor list the question about possibility to obtain that kind of information from arrays. 

Juan 

--------------------------------------------------------------- 
Juan Fernandez Tajes, ph. D 
Grupo XENOMAR 
Departamento de Biología Celular y Molecular 
Facultad de Ciencias-Universidade da Coruña 
Tlf. +34 981 167000 ext 2030 
e-mail: jfernandezt@udc.es 
---------------------------------------------------------------- 



De: "Benilton Carvalho" <beniltoncarvalho@gmail.com> 
Para: "Juan Fernández Tajes" <jfernandezt@udc.es> 
CC: "bioconductor" <bioconductor@r-project.org> 
Enviados: Viernes, 28 de Septiembre 2012 12:34:23 
Asunto: Re: [BioC] Analyzing expression Affymetrix Hugene1.0.st array 

Juan, 

To call a gene (differentially) 'expressed', you need to compare its 
expression to some baseline. The most basic workflow for such task 
starts by defining a control group and the group that you want to 
analyse (which I'll call here "group of interest"). After 
preprocessing all the samples, you prepare a design matrix and fit 
linear models to assess the hypothesis of differential expression (ie. 
you compare the expression of the group of interest to the expression 
of the control group). This gives you (variants of) t-tests, which 
combined with a threshold gives you a set of candidates for 
differential expression. 

That said, you need to define what is the control group for your 
experiment and proceed with the statistical procedures for hypothesis 
testing. 

benilton 


On 28 September 2012 11:10, Juan Fernández Tajes <jfernandezt@udc.es> wrote: 
> Dear List, 
> 
> I´m working with expression data obtained from Affymetrix HuGene 1.0 st array. I´m interested in knowing how many genes are expressed in chromosome 16. Surprisingly, all the genes included (808) in the array and mapped to chromosome have expression values (from 2.01 to 12.4), can I conclude that all these genes are expressed in this tissue? 
> 
> Many thanks in advance 
> 
> Here is my code: 
> 
> 
> geneCELs.N <- list.celfiles(getwd(), full.names=T) 
> affyGeneFS.N <- read.celfiles(geneCELs.N) 
> myAB.N <- affyGeneFS.N 
> sampleNames(myAB.N) <- sub("\\.CEL$", "", sampleNames(myAB.N)) 
> sampleNames(myAB.N) <- sub("\\.CEL$", "", sampleNames(myAB.N)) 
> metadata_array.N <- read.delim(file="metadata.txt", header=T, sep="\t") 
> rownames(metadata_array.N) <- metadata_array.N$Sample_ID 
> phenoData(myAB.N) <- new("AnnotatedDataFrame", data=metadata_array.N) 
> myAB.N_rma <- rma(myAB.N, target="core") 
> annotation(myAB.N_rma) <- "hugene10sttranscriptcluster.db" 
> 
> ppc <- function(x) paste("^", x, sep="") 
> myFindMap <- function(mapEnv, which){ 
> myg <- ppc(which) 
> a1 = eapply(mapEnv, function(x) 
> grep(myg, x, value=T)) 
> unlist(a1) 
> } 
> chr16.N <- myFindMap(hugene10sttranscriptclusterCHR, 16) 
> chr16.N <- as.data.frame(chr16.N) 
> chr16.N$probes <- rownames(chr16.N) 
> probes.chr16.N <- chr16.N$probes 
> sel.N <- match(probes.chr16.N, featureNames(myAB.N_rma), nomatch=0) 
> es2_chr16.N <- myAB.N_rma[sel.N,] 
> data.exprs.N <- as.data.frame(exprs(es2_chr16.N)) 
> g.N <- featureNames(es2_chr16.N) 
> linked.N <- links(hugene10sttranscriptclusterSYMBOL) 
> data.exprs.N.symbol <- merge(data.exprs.N, linked.N, by.x="row.names", by.y="probe_id") 
> row.names(data.exprs.N.symbol) <- data.exprs.N.symbol[[1]] 
> data.exprs.N.symbol <- data.exprs.N.symbol[, -1] 
> data.exprs.N.symbol$Mean.Exprs <- rowMeans(data.exprs.N.symbol[, 1:12]) 
> 
> 
> Juan 
> 
> 
> --------------------------------------------------------------- 
> Juan Fernandez Tajes, ph. D 
> Grupo XENOMAR 
> Departamento de Biología Celular y Molecular 
> Facultad de Ciencias-Universidade da Coruña 
> Tlf. +34 981 167000 ext 2030 
> e-mail: jfernandezt@udc.es 
> ---------------------------------------------------------------- 
> 
> 
> 
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> 
> 
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