Using Bioconductor To Analyse Microarray Data: Difference between revisions
| Line 40: | Line 40: | ||
<pre> | <pre> | ||
library(limma) #load limma package | 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 | pData(eset) #to see phenotype annotation data | ||
design <- model.matrix(~(c(1,1,1,1,1,1,1,0,0,0,0,0,0,0,0)),eset) #set design matirx | design <- model.matrix(~(c(1,1,1,1,1,1,1,0,0,0,0,0,0,0,0)),eset) #set design matirx | ||
colnames(design) <- c("resistant","sensitive") # give names to the treatment groups | colnames(design) <- c("resistant","sensitive") # give names to the treatment groups | ||
design #check the design matrix | design #check the design matrix | ||
fit <- lmFit(eset,design) | fit <- lmFit(eset.norm,design) #Fit data to linear model | ||
fit.eb <- eBayes(fit) | fit.norm.eb <- eBayes(fit.norm) #Empirical Bayes | ||
write.csv(fit.eb, file="filename.csv") #write to CSV file | write.csv(fit.norm.eb, file="filename.csv") #write to CSV file | ||
</pre> | </pre> | ||