From raw data to gene annotations

Transcription

1 From raw data to gene annotations Laurent Gautier (Modified by C. Friis) 1 Process Affymetrix data First of all, you must download data files listed at and place them in a directory. Convenience, we assume that they are located in the directory from where you started R. > working.dir <- "." Replace it by any alternative location you might have chosen for your files. As usual one needs to load packages for the current R session: > library(affy) Welcome to Bioconductor Vignettes contain introductory material. simply type: openvignette() For details on reading vignettes, see the openvignette help page. To view, An introduction to the affy package can guide you a bit through the package. It is distributed with the package itself, or is available on the website for Bioconductor ( select Vignettes in the left frame). 1.1 Make raw data objects We will need an instance of class phenodata (this object contains covariate information for the samples). The function read.phenodata can be used to build such an object from a file. > my.filenames <- list.files(working.dir, "CEL") > my.pheno <- read.phenodata(filename = file.path(working.dir, + "pheno.tab"), header = TRUE) In its current state this function is not very fool-proof when reading data from a file. It is advised to have the name of the raw data files in the covariate data file in order to check that we get things in the right order. > cbind(my.filenames, pdata(my.pheno)) 1

2 my.filenames Filename Condition 1 starv1.cel starv1.cel Starved 2 starv2.cel starv2.cel Starved 3 starv3.cel starv3.cel Starved 4 stim1.cel stim1.cel Stimulated 5 stim2.cel stim2.cel Stimulated 6 stim3.cel stim3.cel Stimulated > my.names.in.pdatafile <- pdata(my.pheno)["filename"] > any(my.filenames!= my.names.in.pdatafile) [1] FALSE Alternatively, you can enter information about the experiments by hand: my.pheno <- read.phenodata(samplenames=my.filenames, widget=true) We need also to read raw data in CEL files and store the information they contain in an instance of class AffyBatch. > abatch <- ReadAffy(filenames = file.path(working.dir, my.filenames), + phenodata = my.pheno) > class(abatch) [1] "AffyBatch" attr(,"package") [1] "affy" 1.2 Pre-process The easiest way to go from raw probe intensities to expression values uses the function expresso: > eset <- expresso(abatch, bg.correct = FALSE, normalize = TRUE, + normalize.method = "qspline", pmcorrect.method = "pmonly", + summary.method = "medianpolish") normalization: qspline PM/MM correction : pmonly expression values: medianpolish normalizing...[1] "samples= 282 k= 5 first= 999" [1] "sampling(array= 1 iter= 1 off= )" [1] "sampling(array= 1 iter= 2 off= )" [1] "sampling(array= 1 iter= 3 off= )" [1] "sampling(array= 1 iter= 4 off= )" [1] "sampling(array= 1 iter= 5 off= )" [1] "sampling(array= 2 iter= 1 off= )" [1] "sampling(array= 2 iter= 2 off= )" [1] "sampling(array= 2 iter= 3 off= )" [1] "sampling(array= 2 iter= 4 off= )" [1] "sampling(array= 2 iter= 5 off= )" [1] "sampling(array= 3 iter= 1 off= )" 2

3 [1] "sampling(array= 3 iter= 2 off= )" [1] "sampling(array= 3 iter= 3 off= )" [1] "sampling(array= 3 iter= 4 off= )" [1] "sampling(array= 3 iter= 5 off= )" [1] "sampling(array= 4 iter= 1 off= )" [1] "sampling(array= 4 iter= 2 off= )" [1] "sampling(array= 4 iter= 3 off= )" [1] "sampling(array= 4 iter= 4 off= )" [1] "sampling(array= 4 iter= 5 off= )" [1] "sampling(array= 5 iter= 1 off= )" [1] "sampling(array= 5 iter= 2 off= )" [1] "sampling(array= 5 iter= 3 off= )" [1] "sampling(array= 5 iter= 4 off= )" [1] "sampling(array= 5 iter= 5 off= )" [1] "sampling(array= 6 iter= 1 off= )" [1] "sampling(array= 6 iter= 2 off= )" [1] "sampling(array= 6 iter= 3 off= )" [1] "sampling(array= 6 iter= 4 off= )" [1] "sampling(array= 6 iter= 5 off= )" done ids to be processed... This may take several minutes... You can choose different pre-processing methods. A point and click way to do it is: eset <- expresso(abatch, widget=true) Now we have expression values stored in an instance of class exprset. 2 cdna array 2.1 Make raw data objects Raw data from cdna arrays can be found in several different formats. The package marrayinput addresses this issue. We use a pre-loaded dataset for the examples. > library(marrayclasses) > data(swirl) 2.2 Pre-process Normalization comes next: > library(marraynorm) Loading required package: stepfun Loading required package: marrayinput > swirl.n <- manormmain(swirl) 3

4 Note that there exist several methods for normalizing your data. Please refer to the documentation in marraynorm if you have interest in the matter. Often people work with log-ratios. Log ratios are stored in the slot mam of an instance of class marraynorm. An instance of class exprset can be built simply, as show below. First, we define an helpful function: > getphenodata.marraynorm <- function(ma) { + mat <- matargets(ma) + dataf <- mainfo(mat) + dataf <- data.frame(labels = malabels(mat), dataf) + names(dataf) <- make.names(names(dataf)) + pd <- new("phenodata", pdata = dataf, varlabels = c(list("labels"), + as.list(names(mainfo(mat))))) + return(pd) + } Then we build the instance of class exprset: > my.m <- mam(swirl.n) > swirl.gnames <- magnames(swirl.n) > swirl.genelabels <- malabels(swirl.gnames) > rownames(my.m) <- swirl.genelabels > my.pheno <- getphenodata.marraynorm(swirl.n) > eset2 <- new("exprset", exprs = my.m, phenodata = my.pheno) The reader can observe that this is a very artificial example. The swirl dataset contains what is called a dye-swap set of experiments. This means that for each sample, the labelling is performed with alternatively with Cy3 and Cy5. The way to build an object of class exprset will depend on your design. Note that in some spotting patterns, the same gene is spotted several times across the chip. This is done to add robustness to the measurement. If we proceed like above, we might have several times the same gene in different rows. This is the case for the swirl dataset. This is not necessarily bad, but one should be aware of it when analyzing the data. Having several measurements for each gene can be seen as very similar in data structure to Affymetrix chips having several probes per gene. One might wish to summarize all the probes intensities for the same gene into one numerical value. The example below shows how to take the mean of the repeated measurement. > swirl.genelabels <- as.factor(swirl.genelabels) > allrows.pos <- seq(along = swirl.genelabels) > genes.index <- tapply(allrows.pos, swirl.genelabels, I) 3 Expressions values A large percentage of microarray data available on the internet, often as supplementary material for published articles, are not raw data but already preprocessed expression values or in the case of two colors arrays ratios or log-ratios between the green and the red channel. This is not a perfect thing, since once 4

5 findings are made with the data one cannot verify with raw data the possibility of experimental artefacts. A instance of class exprset can be build from scratch, or using the function read.exprset. Note that an instance of class exprset can contain expression values, log-ratio of expression values, of whatever measurement one may like to have. 4 From gene to annotation The package annotate provides utilities to query public databases over the internet. First it needs to be loaded. > library(annotate) We leave how to select interesting genes to the reader (and to the other tutorials). To continue with the examples we take three random genes: > set.seed(123) > my.interestgenes <- sample(genenames(eset2), 3) > print(my.interestgenes) [1] "18-B21" "18-H17" "12-N19" Unfortunately the gene names are not GENBANK entries. A query on GEN- BANK returns: > genbank(my.interestgenes) Loading required package: XML [1] "No XML records available for accession number 18-B21%2c18-H17%2c12-N19" NULL Things are a little more complex. Sometimes the gene names are local identifier for the probes on the chip. One needs to map the identifiers on the chips to identifiers on entries in databases. The package AnnBuilder is a package that can build packages, and more specifically annotation package. The procedure can be a little complex, as it requires to install different databases. Interested readers will refer to the documentation contained in the package. Affymetrix chips are commercial chips produced in large quantities, therefore building annotation packages for the popular chip types sold can benefit a very large number of research teams. This is done by Zhang JianHua, and the packages are available on the Bioconductor website (section meta-data). We had an AffyBatch called abatch. The name of the corresponding chip type is: > abatch@cdfname [1] "Hu6800" The annotation package is: > library(hu6800) 5

6 (Other annotation packages are available on the Bioconductor website, or are already available on the CBS computer you are working with). Each annotation package contains several associative arrays (to map an indentifier to its corresponding identifier in an other database). To convert the Affymetrix identifiers into an identifier of interest, the function multiget is handy: > my.genes <- sample(genenames(eset), 3) > my.pubmedids <- multiget(my.genes, envir = hu6800pmid) > print(my.pubmedids) $"X78121_at" [1] " " " " " " " " " " " " " " $"X99586_s_at" [1] " " " " " " " " " " " " [7] " " " " " " " " " " " " [13] " " " " " " " " " " " " [19] " " " " " " $"D17516_at" [1] " " " " " " " " " " " " " " [8] " " " " > my.genbankids <- multiget(my.genes, envir = hu6800accnum) > print(my.genbankids) $"X78121_at" [1] "X78121" $"X99586_s_at" [1] "X99586" $"D17516_at" [1] "D17516" Note that the association is not one to one. One entry in a databank can be associated with several entries in an other databank. Now that we have identifiers associated with the genes of interest, the corresponding information in databases can be queried. If one wishes to query databases over the internet, functions exist to do so and display the result in a web browser: for (i in seq(along=my.genbankids)) { current.gene <- names(my.genbankids)[i] readline(paste(current.gene, "- press enter to continue.")) genbank(my.genbankids, disp="browser") } or return the results in XML format: 6