Difference between revisions of "Using Bioconductor To Analyse Microarray Data"
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[[Category:R]] | [[Category:R]] | ||
[[Category:Bioinformatics]] | [[Category:Bioinformatics]] | ||
| + | [[Category: Bioconductor]] | ||
==Software Requirements== | ==Software Requirements== | ||
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**Biobase | **Biobase | ||
**GEOquery - [http://www.bioconductor.org/packages/1.8/bioc/html/GEOquery.html] | **GEOquery - [http://www.bioconductor.org/packages/1.8/bioc/html/GEOquery.html] | ||
| + | **Limma | ||
<pre> | <pre> | ||
source("http://www.bioconductor.org/biocLite.R") | source("http://www.bioconductor.org/biocLite.R") | ||
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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 | ||
| Line 38: | Line 40: | ||
*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]] | *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> | <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 | pData(eset) #to see phenotype annotation data | ||
| − | design | + | 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(" | + | colnames(design) <- c("obese","lean") # give names to the treatment groups |
design #check the design matrix | 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> | </pre> | ||
Latest revision as of 15:34, 2 September 2009
Contents
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(~ -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
Clustering Analysis
Bioconductor packages can calculate distance matrices:
hc <- hclust(dist(t(exprs(eset.norm)))) plot(hc)
