pathway.r

pathway.r

# These are bioconductor packages. See http://www.bioconductor.org/install/ for installation instructions
library(Biobase)
library(GEOquery)
library(limma)
library(SPIA)
library(hgu133plus2.db)

# load series and platform data from GEO: 
# http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE4107
eset <- getGEO("GSE4107", GSEMatrix =TRUE)[[1]] 

# log transform 
exprs(eset) <- log2(exprs(eset))

# set up a design matrix and contrast matrix
design <- model.matrix(~0+as.factor(c(rep(1,12), rep(0,10))))
colnames(design) <- c("cancer","normal")
contrast.matrix <- makeContrasts(cancer_v_normal=cancer-normal, levels=design)

# run the analysis with empirical Bayes moderated standard errors
fit <- lmFit(eset,design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)


# get useful information for the top 25 genes
top <- topTable(fit2, coef="cancer_v_normal", number=nrow(fit2), adjust.method="fdr")
top <- na.omit(subset(top, select=c(ID, logFC, adj.P.Val)))
top$ID <- as.character(top$ID)

# annotate with entrez info
top$ENTREZ<-unlist(as.list(hgu133plus2ENTREZID[top$ID]))
top<-top[!is.na(top$ENTREZ),]
top<-top[!duplicated(top$ENTREZ),]
top$SYMBOL<-unlist(as.list(hgu133plus2SYMBOL[top$ID]))
top<-top[!is.na(top$SYMBOL),]
top<-top[!duplicated(top$SYMBOL),]

top[1:20,]