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.
1 2 3 4 5 6 7 8 9  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 pvalues of the Wilcoxon and
KruskalWallis 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 pvalue for statistical significance. 
cutoffOn 
The value the cutoff is applied to, either "cor" for correlation or "pval" for pvalue 
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 pvalue results
will be located. Default is 
typeImage 
The type of image file to be used. 
filename 
The name of the HTML file containing the pvalues of the tests, if NULL no file is created. 
This function writes an HTML file containing the
correlation values and test pvalues as a an array of
dimensions 'variables * components' containing the
pvalues of the tests. When a pvalue 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 subdirectory "plots/" of path
.
Each image is named by indexofcomponent_var.png.
Returns A data.frame of dimensions 'components x variables' containing the pvalues of the nonparametric tests (Wilcoxon or KruskalWallis 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  ## 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 pvalues 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)

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