clusVarAnalysis: Tests association between clusters of samples and variables
In MineICA: Analysis of an ICA decomposition obtained on genomics data

Description Usage Arguments Details Value Author(s) See Also Examples

From a clustering of samples performed according to their contribution to each component, this function computes the chi-squared test of association between each variable level and the cluster, and summarizes the results in an HTML file.

  clusVarAnalysis(icaSet, params, resClus, keepVar,
    keepComp, funClus = "",
    adjustBy = c("none", "component", "variable"),
    method = "BH", doPlot = FALSE,
    cutoff = params["pvalCutoff"],
    path = paste(resPath(params), "clus2var/", sep = ""),
    onlySign = TRUE, typeImage = "png",
    testBy = c("variable", "level"), filename)

`icaSet`	An object of class `IcaSet`
`params`	An object of class `MineICAParams` providing the parameters of the analysis
`resClus`	A list of numeric vectors indexed by sample IDs, which specifies the sample clusters. There must be one clustering by component of `icaSet`. The names of the list must correspond to the component indices.
`keepVar`	The variable labels to be considered, i.e a subset of the variables of icaSet available in `varLabels(icaSet)`.
`keepComp`	A subset of components available in `indComp(icaSet)` to be considered, if missing all components are used.
`funClus`	The name of the function used to perform the clustering (just for text in written files).
`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.
`testBy`	Chi-square tests of association can be performed either by `"variable"` (one test by variable, default) or by variable `"level"` (as many tests as there are annotation levels).
`method`	The correction method, see `p.adjust` for details, default if `"BH"` for Benjamini & Hochberg.
`doPlot`	If TRUE, the barplots showing the distribution of the annotation levels among the clusters are plotted and the results are provided in an HTML file 'cluster2annot.htm', else no plot is created.
`cutoff`	The threshold for statistical significance.
`filename`	File name for test results, if `doPlot=TRUE` will be an HTML file else will be a 'txt' file. If missing when `doPlot=TRUE`, will be "clusVar".
`path`	A directory _within resPath(params)_ where the outputs are saved if `doPlot=TRUE`, default is `'cluster2annot/'`.
`onlySign`	If TRUE (default), only the significant results are plotted.
`typeImage`	The type of image file where each plot is saved.

When doPlot=TRUE, this function writes an HTML file containing the results of the tests as a table of dimension 'variable levels x components' which contains 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 barplot displaying the distribution of the clusters across the levels of the variables.

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

This function returns a list whose each element gives, for each component, the results of the association chi-squared tests between the clusters and the annotation levels.

Anne Biton

clusterSamplesByComp

## load an example of IcaSet
data(icaSetCarbayo)
## build object of class MineICAParams
params <- buildMineICAParams(resPath="carbayo/")

## cluster samples according to the columns of the mixing matrix A with kmeans in 2 groups
resClus <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="kmeans",
                               clusterOn="A", nbClus=2)$clus

## specify directory for the function outputs (here same directory as the default one)
## this directory will be created by the function in resPath(params)
dir <- "clus2var/"

## compute chi-square tests of association, p-value are not adjusted (adjustBy="none"),
# test results are written in txt format (doPlot=FALSE and filename not missing)
resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, funClus="kmeans",
                          adjustBy="none", doPlot=FALSE, path=dir, filename="clusVarTests")

## Not run: 
## compute chi-square tests of association, p-value are not adjusted (adjustBy="none"),
# write results and plots in HTML files (doPlot=TRUE)
resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, funClus="kmeans",
                          path=dir, adjustBy="none", doPlot=TRUE, filename="clusVarTests")

## compute chi-square tests of association by only considering a subset of components and variables,
# adjust p-values by component  (adjustBy="component"),
# do not write results (doPlot=FALSE and filename is missing).
resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, keepComp = 1:10,
                          keepVar=c("GENDER","STAGE"), funClus="kmeans", adjustBy="none",
                          doPlot=FALSE)

## End(Not run)