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

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m (Obtaining GEO Datasets)
m (Microarray Analysis)
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==Microarray Analysis==
 
==Microarray Analysis==
*set up design matrix.  Use a different integer for each treatment group.
+
*set up design matrix.  Use a different integer for each treatment group. The following example is for a contrast between the first four groups and the last four 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>
 
pData(eset)  #to see phenotype annotation data
 
pData(eset)  #to see phenotype annotation data
design <- model.matrix(~0+factor(c(1,1,1,1,2,2,2,2)),eset)  #for four replicates of each treatment group
+
design <- model.matrix(~(c(0,0,0,0,1,1,1,1)),eset)  #for four replicates of each treatment group,
 
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
 
</pre>
 
</pre>

Revision as of 00:55, 27 July 2009


Software Requirements

  • R, get from [CRAN]
  • Bioconductor, get from [Bioconductor]
  • Bioconductor packages. Install as needed:
    • Biobase
    • GEOquery - [1]
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("GDS162")  #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 four groups and the last four groups. For details on other design matrices see chapter 8 of [limma User Guide]
pData(eset)  #to see phenotype annotation data
design <- model.matrix(~(c(0,0,0,0,1,1,1,1)),eset)  #for four replicates of each treatment group,
colnames(design) <- c("resistant","sensitive")  # give names to the treatment groups
design  #check the design matrix