inst/doc/gettingPbcmcData.R

In MIGSA: Massive and Integrative Gene Set Analysis

### R code from vignette source 'gettingPbcmcData.Rnw'

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### code chunk number 1: gettingData (eval = FALSE)
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## library(limma);
## library(pbcmc);
## 
## # datasets included in BioConductor repository
## libNames <- c("mainz", "nki", "transbig", "unt", "upp", "vdx");
## 
## # let's load them!
## pbcmcData <- lapply(libNames, function(actLibName) {
##     print(actLibName);
##     
##     # the pbcmc package provides an easy way to download and classify them
##     actLib <- loadBCDataset(Class=PAM50, libname=actLibName, verbose=FALSE);
##     actLibFilt <- filtrate(actLib, verbose=FALSE);
##     actLibFilt <- classify(actLibFilt, std="none", verbose=FALSE);
##     actSubtypes <- classification(actLibFilt)$subtype;
##     
##     # get the expression matrix and the annotation
##     actExprs <- exprs(actLib);
##     actAnnot <- annotation(actLib);
##     
##     # we recommend working allways with Entrez IDs, let's match them with 
##     # expression matrix rownames (and modify them)
##     if (all(actAnnot$probe == rownames(actExprs))) {
##         actExprs <- actExprs[!is.na(actAnnot$EntrezGene.ID),];
##         actAnnot <- actAnnot[!is.na(actAnnot$EntrezGene.ID),];
##         rownames(actExprs) <- as.character(actAnnot$EntrezGene.ID);
##     } else {
##         matchedEntrez <- match(rownames(actExprs), actAnnot$probe);
##         # all(rownames(actExprs) %in% actAnnot$probe == !is.na(matchedEntrez));
##         
##         stopifnot(all(
##             actAnnot$probe[!is.na(matchedEntrez)] ==
##             rownames(actExprs)[!is.na(matchedEntrez)]));
##         
##         actExprs <- actExprs[!is.na(matchedEntrez),];
##         actAnnot <- actAnnot[!is.na(matchedEntrez),];
##         stopifnot(all(actAnnot$probe == rownames(actExprs)));
##         actExprs <- actExprs[!is.na(actAnnot$EntrezGene.ID),];
##         actAnnot <- actAnnot[!is.na(actAnnot$EntrezGene.ID),];
##         rownames(actExprs) <- as.character(actAnnot$EntrezGene.ID);
##     }
##     
##     # average repeated genes expression
##     actExprs <- avereps(actExprs);
##     
##     stopifnot(all(colnames(actExprs) == names(actSubtypes)));
##     # filtrate only these two conditions
##     actExprs <- actExprs[, actSubtypes %in% c("Basal", "LumA")];
##     actSubtypes <- as.character(
##         actSubtypes[actSubtypes %in% c("Basal", "LumA")]);
##     
##     return(list(geneExpr=actExprs, subtypes=actSubtypes));
## })
## names(pbcmcData) <- libNames;


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### code chunk number 2: validateData (eval = FALSE)
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## # save the just created pbcmcData to newPbcmcData
## newPbcmcData <- pbcmcData;
## 
## library(MIGSAdata);
## 
## # and load the MIGSAdata one.
## data(pbcmcData);
## all.equal(newPbcmcData, pbcmcData);


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### code chunk number 3: Session Info
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sessionInfo()