Calculate distance between two vectors, rows of one matrix/dataframe, or rows of two matrices/dataframes.

Description

This function does some simple looping to allow x and y to be various combinations of vectors and matrices/dataframes.

Usage

phenoDist(x, y = NULL, bins = 10, vectorDistFun = vectorWeightedDist, 
    ...)

Arguments

Value

a matrix of distances between pairs of rows of x (if y is unspecified), or between all pairs of rows between x and y (if both are provided).

Author(s)

Levi Waldron, Markus Riester, Marcel Ramos

Examples

example("phenoFinder")

pdat1 <- pData(esets2[[1]])
pdat2 <- pData(esets2[[2]])

## Use phenoDist() to calculate a weighted distance matrix
distmat <- phenoDist(as.matrix(pdat1), as.matrix(pdat2))
## Note outliers with identical clinical data, these are probably the same patients:
graphics::boxplot(distmat)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 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/phenoDist.Rd_%03d_medium.png", width=480, height=480)
> ### Name: phenoDist
> ### Title: Calculate distance between two vectors, rows of one
> ###   matrix/dataframe, or rows of two matrices/dataframes.
> ### Aliases: phenoDist
> 
> ### ** Examples
> 
> example("phenoFinder")

phnFnd> library(curatedOvarianData)
Loading required package: affy

phnFnd> data(GSE32063_eset)

phnFnd> data(GSE17260_eset)

phnFnd> esets2 <- list(JapaneseB=GSE32063_eset,
phnFnd+                 Yoshihara2010=GSE17260_eset)

phnFnd> ## standardize the sample ids to improve matching based on clinical annotation
phnFnd> esets2 <- lapply(esets2, function(X){
phnFnd+     X$alt_sample_name <- paste(X$sample_type, gsub("[^0-9]", "", X$alt_sample_name), sep="_")
phnFnd+ 
phnFnd+ ## Removal of columns that cannot possibly match also helps duplicated patients to stand out
phnFnd+     pData(X) <- pData(X)[, !grepl("uncurated_author_metadata", colnames(pData(X)))]
phnFnd+     X <- X[, 1:20]  ##speed computations
phnFnd+     return(X) })

phnFnd> ## See first six samples in both rows and columns
phnFnd> 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
> 
> pdat1 <- pData(esets2[[1]])
> pdat2 <- pData(esets2[[2]])
> 
> ## Use phenoDist() to calculate a weighted distance matrix
> distmat <- phenoDist(as.matrix(pdat1), as.matrix(pdat2))
> ## Note outliers with identical clinical data, these are probably the same patients:
> graphics::boxplot(distmat)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>