quantVarAnalysis: Correlation between variables and components.
In MineICA: Analysis of an ICA decomposition obtained on genomics data

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

View source: R/functions_comp2annottests.R

This function tests if numeric variables are correlated with components.

  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")

`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.

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.

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.

Anne Biton

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

## 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)