quantVarAnalysis {MineICA} | R Documentation |
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
|
icaSet |
An object of class
|
keepVar |
The variable labels to be considered, must
be a subset of |
keepComp |
A subset of components, must be included
in |
keepSamples |
A subset of samples, must be included
in |
adjustBy |
The way the p-values of the Wilcoxon and
Kruskal-Wallis tests should be corrected for multiple
testing: |
method |
The correction method, see
|
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
|
colours |
A vector of colours indexed by the
variable levels, if missing the colours are automatically
generated using |
path |
A directory _within resPath(params)_ where
the files containing the plots and the p-value results
will be located. Default is |
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)