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[[Category:R]]
[[Category:Bioinformatics]]
[[Category: Bioconductor]]
==Software Requirements==
*Bioconductor, get from [[http://www.bioconductor.org/download Bioconductor]]
*Bioconductor packages. Install as needed:
**Biobase**GEOquery- [http://www.bioconductor.org/packages/1.8/bioc/html/GEOquery.html]**Limma
<pre>
</pre>
==Obtaining GEO Datasets==
*Open a R terminal
*Load Biobase and GEOquery packages
<pre>
libary(Biobase)
library(GEOquery)
</pre>
*Can load:
**datasets - '''GDS'''
**measurements - '''GSM'''
**platforms - '''GPL'''
**series - '''GSE'''
<pre>
gds <- getGEO("GDS2946") #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 with log2 transformation
pData(eset) #phenotype data
sampleNames(eset) #sample names (GSM)
</pre>
*see [[http://www2.warwick.ac.uk/fac/sci/moac/currentstudents/peter_cock/r/geo/ Peter Cock's Page]] or [[http://www.bioconductor.org/packages/1.8/bioc/html/GEOquery.html GEOquery Documentation]] for more information.
==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(~ -1+factor(c(1,1,1,1,1,1,1,2,2,2,2,2,2,2,2) #set design matirx
colnames(design) <- c("obese","lean") # 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>
