Calculate pairwise similarities of phenoData between samples for a list containing two ExpressionSets

Description

This function acts as a wrapper to phenoDist to handle cases of one ExpressionSet, a list of two identical ExpressionSets, or a list of two different ExpressionSets.

Usage

phenoFinder(eset.pair, separator = ":", ...)

Arguments

Value

A matrix of similarities between the phenotypes of pairs of samples.

Author(s)

Levi Waldron, Markus Riester, Marcel Ramos

Examples

library(curatedOvarianData)
data(GSE32063_eset)
data(GSE17260_eset)
esets2 <- list(JapaneseB=GSE32063_eset,
                Yoshihara2010=GSE17260_eset)

## standardize the sample ids to improve matching based on clinical annotation
esets2 <- lapply(esets2, function(X){
    X$alt_sample_name <- paste(X$sample_type, gsub("[^0-9]", "", X$alt_sample_name), sep="_")

## Removal of columns that cannot possibly match also helps duplicated patients to stand out
    pData(X) <- pData(X)[, !grepl("uncurated_author_metadata", colnames(pData(X)))]
    X <- X[, 1:20]  ##speed computations
    return(X) })

## See first six samples in both rows and columns
phenoFinder(esets2)[1:6, 1:6]

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> library(doppelgangR)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: BiocParallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/doppelgangR/phenoFinder.Rd_%03d_medium.png", width=480, height=480)
> ### Name: phenoFinder
> ### Title: Calculate pairwise similarities of phenoData between samples for
> ###   a list containing two ExpressionSets
> ### Aliases: phenoFinder
> 
> ### ** Examples
> 
> library(curatedOvarianData)
Loading required package: affy
> data(GSE32063_eset)
> data(GSE17260_eset)
> esets2 <- list(JapaneseB=GSE32063_eset,
+                 Yoshihara2010=GSE17260_eset)
> 
> ## standardize the sample ids to improve matching based on clinical annotation
> esets2 <- lapply(esets2, function(X){
+     X$alt_sample_name <- paste(X$sample_type, gsub("[^0-9]", "", X$alt_sample_name), sep="_")
+ 
+ ## Removal of columns that cannot possibly match also helps duplicated patients to stand out
+     pData(X) <- pData(X)[, !grepl("uncurated_author_metadata", colnames(pData(X)))]
+     X <- X[, 1:20]  ##speed computations
+     return(X) })
> 
> ## See first six samples in both rows and columns
> phenoFinder(esets2)[1:6, 1:6]
          GSM432220 GSM432221 GSM432222 GSM432223 GSM432224  GSM432225
GSM795125 0.2351904 0.1014047 0.3525417 0.7274151 0.2189890 0.27397077
GSM795126 0.5404524 0.2588727 0.4083015 0.4079720 0.2927870 0.74123368
GSM795127 0.3791279 0.5008562 0.4983502 0.4981226 0.6385506 0.04416984
GSM795128 0.2351904 0.1014047 0.3525417 0.3523760 0.2189890 0.27397077
GSM795129 0.1076309 0.2395470 0.2190910 0.2189890 0.3643260 0.16030839
GSM795130 0.2603947 0.1344290 0.1077761 0.1076793 0.2489234 0.29544860
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>