Introduction to microarray data analysis with Bioconductor Katherine S. Pollard March 11, 2004 © Copyright 2004, all rights reserved

Bioconductor oOpen source and open development R software project for the analysis and comprehension of biomedical and genomic data. –Gene expression arrays (cDNA, Affymetrix) –Pathway graphs –Genome sequence data oStarted in 2001 by Robert Gentleman, Dana Farber Cancer Institute. oAbout 25 core developers, at various institutions in the US and Europe. oTools for integrating biological metadata from the web (annotation, literature) in the analysis of experimental data.

Websites oBioconductor: –software, data, and documentation; –training materials from short courses; –mailing list. oR: –software; –documentation; –RNews.

Basic R Commands oWorking directory/file path: File – Change dir > setwd(“C:/cygwin”) oList objects in session: Misc – List objects > ls() oDelete objects from session: Misc – Remove all objects > rm(my.matrix) oRun a script: File – Source R code > source(“mycode.R”) oStopping R: File - Exit > q()

Getting Help o Details about a specific command whose name you know (input arguments, options, algorithm): > ? t.test > help(t.test) > example(t.test) > t.test o Information about commands containing a certain text string: > apropos(“test”) > help.search(“test”)

Packages & Vignettes oLoad a package library: Packages menu > library(marrayTools) oRun the package vignette: > library(tkWidgets) > vExplorer() > openVignette() oRead the Vignette PDF file oLook at Short Courses and Lab Materials

Storing Data o Every R object (or the whole current working environment) can be stored into and restored from a file with the commands “save” and “load”. OR by using the File menu. > save(x, file=“x.RData”) > load(“x.RData”) > save.image(“splicingArrays.RData”) o These files are portable between MS- Windows, Unix, Mac versions of R.

Importing and Exporting Data o There are many ways to get data in and out. o Most programs (e.g. Excel), as well as humans, know how to deal with rectangular tables in the form of tab-delimited text files. > x <- read.delim(“filename.txt”) Also: read.table, read.csv, scan > write.table(x, file=“x.txt”, sep=“\t”) Also: write.matrix, write

Script to import GenePix data library(marrayTools) importGPR<-function(gal,details){ g.info<-read.marrayInfo(fname=gal,info.id=4:5,labels=5) a.info<-read.marrayInfo(fname=details,labels=2) grid<-read.marrayLayout(fname=gal,ngr=4,ngc=4,nsr=24, nsc=25,pl.col=7,ctl.col=6) data<-read.GenePix(layout=grid,targets=a.info, gnames=g.info,name.Gf="F532 Median", name.Rf="F635 Median") return(data) } data.raw<-importGPR(galfile,detailsfile) o Note: If.gal file has n lines at top, before data begins, use skip=n o Note: read.GenePix will read ALL.gpr files in current directory. To read certain files (and to specify the order) use fname argument.

Working with log ratios oLoess normalization by print tip: data.norm<-maNormMain(data.raw) ratios<-as(data.norm,"exprSet") oArray statistics: apply(exprs(ratios),2,summary) apply(maGb(data.raw),2,median,na.rm=TRUE) oCombine replicate spots on an array: meanM<-aggregate(exprs(ratios), list(maLabels(maGnames(data.norm))), mean, na.rm=TRUE) oExport normalized log 2 ratios: write.table(meanM,“Mvals.txt”,sep=“\t”,row.names=F)

Useful R/BioC Packages marrayTools, marrayPlots Spotted cDNA array analysis affy Affymetrix array analysis vsn Variance stabilization annotate Link microarray data to metadata on the web ctest Statistical tests genefilter, limma, multtest, siggenes Gene filtering (e.g.: differential expression) mva, cluster, clust Clustering class, rpart, nnet Classification

Acknowledgments Workshop materials developed with Robert Gentleman, Harvard Sandrine Dudoit, UC Berkeley Bioconductor core developers include Vince Carey, Harvard Yongchao Ge, Mount Sinai School of Medicine Robert Gentleman, Harvard Jeff Gentry, Dana-Farber Cancer Institute Rafael Irizarry, Johns Hopkins Yee Hwa (Jean) Yang, UCSF Jianhua (John) Zhang, Dana- Farber Cancer Institute Sandrine Dudoit, UC Berkeley