makeCanonicalCorrelationAnalysis: Calculate CCA on data returned from decoration methods.

In cmujzbit/OmicsON: OmicsON is a package implementing workflow for finding associations acrossomics data sets

Description

Function is used to easily perform CCA analysis on functional interactions data frames, those data frames are returned from OmicsON::createFunctionalInteractionsDataFrame which works directly on data returned from functions responsible for decoration. To close workflow, you can then use dedicated plotting function on returned data, which is OmicsON::plotCanonicalCorrelationAnalysisResults.

Usage

makeCanonicalCorrelationAnalysis(xNamesVector, yNamesVector, XDataFrame,
  YDataFrame, xCutoff = 1, yCutoff = 1, scalingFactor = 1)

Arguments

xNamesVector	A vector of names from functional interactions data frame. Mostly genes symbols.
yNamesVector	A vector of names from functional interactions data frame. Mostly root column.
XDataFrame	A data frame with data to CCA analysis. This argument is strongly connected with xNamesVector arg.
YDataFrame	A data frame with data to CCA analysis. This argument is strongly connected with yNamesVector arg.
xCutoff	You can use it to remove highly correlated variables on XDataFrame.
yCutoff	You can use it to remove highly correlated variables on YDataFrame.
scalingFactor	scaling factor for input data.

Value

list with CCA analysis data.

Examples

ontology2GenesSymboleFromEnsembleFunctionalInteractions <- OmicsON::createFunctionalInteractionsDataFrame(
decoratedByReactome,
singleIdColumnName = 'ontologyId',
idsListColumnName = 'genesSymbolsFromEnsemble')
ccaResultsEnsemble <- OmicsON::makeCanonicalCorrelationAnalysis(
xNamesVector = ontology2GenesSymboleFromEnsembleFunctionalInteractions$genesSymbolsFromEnsemble,
yNamesVector = ontology2GenesSymboleFromEnsembleFunctionalInteractions$root,
XDataFrame = transcriptomicsInputData,
YDataFrame = lipidomicsInputData, xCutoff = 0.5, yCutoff = 0.75)

cmujzbit/OmicsON documentation built on May 12, 2020, 8:06 p.m.