Difference between revisions of "Using Bioconductor To Analyse Microarray Data"

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[[Category:R]]
 
[[Category:R]]
 
[[Category:Bioinformatics]]
 
[[Category:Bioinformatics]]
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[[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("GDS162")  #load GDS162 dataset
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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
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*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
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library(affyPLM)  #load affyPLM package
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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)  #for four replicates of each treatment group,
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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("resistant","sensitive")  # give names to the treatment groups
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colnames(design) <- c("obese","lean")  # give names to the treatment groups
 
design  #check the design matrix
 
design  #check the design matrix
−
fit <- lmFit(eset,design)
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fit <- lmFit(eset.norm, design)  #Fit data to linear model
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fit.eb <- eBayes(fit)
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cont.matrix <- makeContrasts(Obese.vs.Lean=obese-lean, levels=design)
 +
fit.cont <- contrasts.fit(fit, cont.matrix)
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fit.cont.eb <- eBayes(fit.norm)  #Empirical Bayes
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write.csv(fit.cont.eb, file="filename.csv")  #write to CSV file
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</pre>
 +
 
 +
==Clustering Analysis==
 +
Bioconductor packages can calculate distance matrices:
 +
<pre>
 +
hc <- hclust(dist(t(exprs(eset.norm))))
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plot(hc)
 
</pre>
 
</pre>

Latest revision as of 15:34, 2 September 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)

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)
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