quantVarAnalysis {MineICA} | R Documentation

## Correlation between variables and components.

### Description

This function tests if numeric variables are correlated with components.

### Usage

      quantVarAnalysis(params, icaSet, keepVar,
        keepComp = indComp(icaSet),
        keepSamples = sampleNames(icaSet),
        adjustBy = c("none", "component", "variable"),
        method = "BH", typeCor = "pearson", doPlot = TRUE,
        onlySign = TRUE, cutoff = 0.4,
        cutoffOn = c("cor", "pval"), colours,
        path = "quantVarAnalysis/", filename = "quantVar",
        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.
`onlySign` | If TRUE (default), only the significant results are plotted.
`cutoff` | A threshold p-value for statistical significance.
`cutoffOn` | The value the cutoff is applied to, either "cor" for correlation or "pval" for p-value
`typeCor` | the type of correlation to be used, one of `c("pearson","spearman","kendall")`.
`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 `"quantVarAnalysis/"`.
`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 correlation values and test p-values 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.

### 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

### See Also

`qualVarAnalysis`, `p.adjust`, `link{writeHtmlResTestsByAnnot}`, `code`

### 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), "comp2annottest/", sep="")

    # Check which variables are numeric looking at the pheno data, here only one  -> AGE
    # pData(icaSetCarbayo)

    ## Perform pearson correlation tests and plots association corresponding
    # to correlation values larger than 0.2
    quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", keepComp=1:2,
                     adjustBy="none", path=dir, cutoff=0.2, cutoffOn="cor")

    ## Not run:
    ## Perform Spearman correlation tests and do scatter plots for all pairs
    quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", adjustBy="none", path=dir,
                     cutoff=0.1, cutoffOn="cor", typeCor="spearman", onlySign=FALSE)

    ## Perform pearson correlation tests and plots association corresponding
    # to p-values lower than 0.05 when 'doPlot=TRUE'
    quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", adjustBy="none", path=dir,
                     cutoff=0.05, cutoffOn="pval", doPlot=FALSE)

    ## End(Not run)

[Package MineICA version 1.26.0 Index]