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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
<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(1,1,1,1,1,1,1,02,02,02,02,02,02,02,0)),eset2) #for four replicates of each treatment group,set 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 modelcont.matrix <- makeContrasts(Obese.vs.Lean=obese-lean, levels=design)fit.cont <- contrasts.fit(fit, cont.matrix)fit.cont.eb <- eBayes(fit.norm) #Empirical Bayeswrite.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>
