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Last data update: 2014.03.03

R: Tests association between qualitative variables and...

qualVarAnalysis

R Documentation

Tests association between qualitative variables and components.

Description

This function tests if the groups of samples formed by the variables are differently distributed on the components, in terms of contribution value (i.e of values in matrix A(icaSet)). The distribution of the samples on the components are represented using either density plots of boxplots. It is possible to restrict the tests and the plots to a subset of samples and/or components.

Usage

  qualVarAnalysis(params, icaSet, keepVar,
    keepComp = indComp(icaSet),
    keepSamples = sampleNames(icaSet),
    adjustBy = c("none", "component", "variable"),
    method = "BH", doPlot = TRUE, typePlot = "density",
    addPoints = FALSE, onlySign = TRUE,
    cutoff = params["pvalCutoff"],
    colours = annot2col(params), path = "qualVarAnalysis/",
    filename = "qualVar", typeImage = "png")

Arguments

`params`	An object of class `MineICAParams` providing the parameters of the analysis.
`icaSet`	An object of class `IcaSet`.
`keepVar`	The variable labels to be considered, must be a subset of `varLabels(icaSet)`.
`keepComp`	A subset of components, must be included in `indComp(icaSet)`. By default, all components are used.
`keepSamples`	A subset of samples, must be included in `sampleNames(icaSet)`. By default, all samples are used.
`adjustBy`	The way the p-values of the Wilcoxon and Kruskal-Wallis tests should be corrected for multiple testing: `"none"` if no p-value correction has to be done, `"component"` if the p-values have to be corrected by component, `"variable"` if the p-values have to be corrected by variable
`method`	The correction method, see `p.adjust` for details, default is `"BH"` for Benjamini & Hochberg.
`doPlot`	If TRUE (default), the plots are done, else only tests are performed.
`addPoints`	If TRUE, points are superimposed on the boxplot.
`typePlot`	The type of plot, either `"density"` or `"boxplot"`.
`onlySign`	If TRUE (default), only the significant results are plotted.
`cutoff`	A threshold p-value for statistical significance.
`colours`	A vector of colours indexed by the variable levels, if missing the colours are automatically generated using `annot2Color`.
`path`	A directory _within resPath(params)_ where the files containing the plots and the p-value results will be located. Default is `"qualVarAnalysis/"`.
`typeImage`	The type of image file to be used.
`filename`	The name of the HTML file containing the p-values of the tests, if NULL no file is created.

Details

This function writes an HTML file containing the results of the tests as a an array of dimensions 'variables * components' containing the p-values of the tests. When a p-value is considered as significant according to the threshold cutoff, it is written in bold and filled with a link pointing to the corresponding plot. One image is created by plot and located into the sub-directory "plots/" of path. Each image is named by index-of-component_var.png. Wilcoxon or Kruskal-Wallis tests are performed depending on the number of groups of interest in the considered variable (argument keepLev).

Value

Returns A data.frame of dimensions 'components x variables' containing the p-values of the non-parametric tests (Wilcoxon or Kruskal-Wallis tests) wich test if the samples groups defined by each variable are differently distributed on the components.

Author(s)

Anne Biton

Examples

## load an example of IcaSet
data(icaSetCarbayo)

## build MineICAParams object
params <- buildMineICAParams(resPath="carbayo/")

## Define the directory containing the results
dir <- paste(resPath(params), "comp2annot/", sep="")

## Run tests, make no adjustment of the p-values,
# for variable grade and components 1 and 2,
# and plot boxplots when 'doPlot=TRUE'.
qualVarAnalysis(params=params, icaSet=icaSetCarbayo, adjustBy="none", typePlot="boxplot",
                keepVar="GRADE", keepComp=1:2, path=dir, doPlot=FALSE)

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(MineICA)
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

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    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

Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: plyr
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Attaching package: 'S4Vectors'

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    rename

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    colMeans, colSums, expand.grid, rowMeans, rowSums


Attaching package: 'IRanges'

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    expand

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    join



Attaching package: 'GOstats'

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    makeGOGraph

Loading required package: cluster
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Attaching package: 'limma'

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    plotMA

Loading required package: mclust
Package 'mclust' version 5.2
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: RColorBrewer
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Attaching package: 'igraph'

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Loading required package: Rgraphviz
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Attaching package: 'Rgraphviz'

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Loading required package: annotate
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Attaching package: 'XML'

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Attaching package: 'annotate'

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    toFile

Loading required package: Hmisc
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Attaching package: 'Hmisc'

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    contents

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Loading required package: fastICA
Loading required package: JADE
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MineICA/qualVarAnalysis.Rd_%03d_medium.png", width=480, height=480)
> ### Name: qualVarAnalysis
> ### Title: Tests association between qualitative variables and components.
> ### Aliases: qualVarAnalysis
> 
> ### ** Examples
> 
> ## load an example of IcaSet
> data(icaSetCarbayo)
> 
> ## build MineICAParams object
> params <- buildMineICAParams(resPath="carbayo/")
> 
> ## Define the directory containing the results
> dir <- paste(resPath(params), "comp2annot/", sep="")
> 
> ## Run tests, make no adjustment of the p-values,
> # for variable grade and components 1 and 2,
> # and plot boxplots when 'doPlot=TRUE'.
> qualVarAnalysis(params=params, icaSet=icaSetCarbayo, adjustBy="none", typePlot="boxplot",
+                 keepVar="GRADE", keepComp=1:2, path=dir, doPlot=FALSE)
               1           2
GRADE 0.01569781 0.006969333
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>