Difference between revisions of "Using Bioconductor To Analyse Microarray Data"
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Davebridges (Talk | contribs) m (→Microarray Analysis) |
Davebridges (Talk | contribs) m (→Obtaining GEO Datasets) |
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**series - '''GSE''' | **series - '''GSE''' | ||
<pre> | <pre> | ||
− | gds <- getGEO(" | + | gds <- getGEO("GDS2946") #load GDS162 dataset |
Meta(gds) #show extracted meta data | Meta(gds) #show extracted meta data | ||
table(gds)[1:10,] #show first ten rows of dataset | table(gds)[1:10,] #show first ten rows of dataset |
Revision as of 01:19, 27 July 2009
Software Requirements
- R, get from [CRAN]
- Bioconductor, get from [Bioconductor]
- Bioconductor packages. Install as needed:
- Biobase
- GEOquery - [1]
- Limma
source("http://www.bioconductor.org/biocLite.R") biocLite("PACKAGE")
Obtaining GEO Datasets
- Open a R terminal
- Load Biobase and GEOquery packages
libary(Biobase) library(GEOquery)
- Can load:
- datasets - GDS
- measurements - GSM
- platforms - GPL
- series - GSE
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)
- see [Peter Cock's Page] or [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 [limma User Guide]
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(~(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 design #check the design matrix fit <- lmFit(eset.norm,design) #Fit data to linear model fit.norm.eb <- eBayes(fit.norm) #Empirical Bayes write.csv(fit.norm.eb, file="filename.csv") #write to CSV file