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[[Category:R]]
[[Category:Bioinformatics]]
[[Category: Bioconductor]]
==Software Requirements==
**Biobase
**GEOquery - [http://www.bioconductor.org/packages/1.8/bioc/html/GEOquery.html]
**Limma
<pre>
source("http://www.bioconductor.org/biocLite.R")
**series - '''GSE'''
<pre>
gds <- getGEO("GDS162GDS2946") #load GDS162 dataset
Meta(gds) #show extracted meta data
table(gds)[1:10,] #show first ten rows of dataset
eset <- GDS2eSet(gds, do.log=TRUE) #convert to expression set, by default obtains annotation (GPL) data and no log with log2 transformation.
pData(eset) #phenotype data
sampleNames(eset) #sample names (GSM)
==Microarray Analysis==
*set up design matrix. Use a different integer for each treatment group. The following example is for a contrast between the first seven groups and the last eight groups. For details on other design matrices see chapter 8 of [[http://www.bioconductor.org/packages/2.3/bioc/vignettes/limma/inst/doc/usersguide.pdf limma User Guide]]
<pre>
library(limma) #load limma package
library(affyPLM) #load affyPLM package
eset.norm <- normalize.ExpressionSet.quantiles(eset) #normalize expression set by quantile method
pData(eset) #to see phenotype annotation data
design <- =model.matrix(~0-1+factor(c(1,1,1,1,1,1,1,2,2,2,2)),eset2,2,2,2) #for four replicates of each treatment groupset design matirxcolnames(design) <- c("resistantobese","sensitivelean") # give names to the treatment groups
design #check the design matrix
fit <- lmFit(eset.norm, design) #Fit data to linear model
cont.matrix <- makeContrasts(Obese.vs.Lean=obese-lean, levels=design)
fit.cont <- contrasts.fit(fit, cont.matrix)
fit.cont.eb <- eBayes(fit.norm) #Empirical Bayes
write.csv(fit.cont.eb, file="filename.csv") #write to CSV file
</pre>
==Clustering Analysis==
Bioconductor packages can calculate distance matrices:
<pre>
hc <- hclust(dist(t(exprs(eset.norm))))
plot(hc)
</pre>
