Using Bioconductor To Analyse Microarray Data - Revision history

Davebrid at 15:34, 2 September 2009

2009-09-02T15:34:25Z

2009-07-27T02:24:42Z

Microarray Analysis

← Older revision		Revision as of 02:24, 27 July 2009
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	eset.norm <- normalize.ExpressionSet.quantiles(eset) #normalize expression set by quantile method		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(~ -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		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		fit <- lmFit(eset.norm, design) #Fit data to linear model
	fit.~~norm~~.eb <- eBayes(fit.norm) #Empirical Bayes		cont.matrix <- makeContrasts(Obese.vs.Lean=obese-lean, levels=design)
	write.csv(fit.~~norm~~.eb, file="filename.csv") #write to CSV file		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>		</pre>

2009-07-27T01:30:17Z

Clustering Analysis

← Older revision		Revision as of 01:30, 27 July 2009
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	<pre>		<pre>
	hc <- hclust(dist(t(exprs(eset.norm))))		hc <- hclust(dist(t(exprs(eset.norm))))
			plot(hc)
	</pre>		</pre>

2009-07-27T01:28:49Z

← Older revision		Revision as of 01:28, 27 July 2009
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	fit.norm.eb <- eBayes(fit.norm) #Empirical Bayes		fit.norm.eb <- eBayes(fit.norm) #Empirical Bayes
	write.csv(fit.norm.eb, file="filename.csv") #write to CSV file		write.csv(fit.norm.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))))
	</pre>		</pre>

2009-07-27T01:19:04Z

Obtaining GEO Datasets

← Older revision		Revision as of 01:19, 27 July 2009
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	**series - '''GSE'''		**series - '''GSE'''
	<pre>		<pre>
	gds <- getGEO("~~GDS162~~") #load GDS162 dataset		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

2009-07-27T01:16:07Z

Microarray Analysis

← Older revision		Revision as of 01:16, 27 July 2009
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	<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>

2009-07-27T01:07:44Z

Microarray Analysis

← Older revision		Revision as of 01:07, 27 July 2009
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	fit <- lmFit(eset,design)		fit <- lmFit(eset,design)
	fit.eb <- eBayes(fit)		fit.eb <- eBayes(fit)
			write.csv(fit.eb, file="filename.csv") #write to CSV file
	</pre>		</pre>

2009-07-27T01:03:29Z

Microarray Analysis

← Older revision		Revision as of 01:03, 27 July 2009
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	library(limma) #load limma package		library(limma) #load limma package
	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,~~		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

2009-07-27T01:01:28Z

Software Requirements

← Older revision		Revision as of 01:01, 27 July 2009
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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")

2009-07-27T01:01:11Z

Microarray Analysis

← Older revision		Revision as of 01:01, 27 July 2009
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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
	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,		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,