R: Standard screening for numeric traits

standardScreeningNumericTrait {WGCNA}

R Documentation

Standard screening for numeric traits

Description

Standard screening for numeric traits based on Pearson correlation.

Usage

standardScreeningNumericTrait(datExpr, yNumeric, corFnc = cor,
                              corOptions = list(use = 'p'),
                              alternative = c("two.sided", "less", "greater"),
                              qValues = TRUE,
                              areaUnderROC = TRUE)

Arguments

`datExpr`	data frame containing expression data (or more generally variables to be screened), with rows corresponding to samples and columns to genes (variables)
`yNumeric`	a numeric vector giving the trait measurements for each sample
`corFnc`	correlation function. Defaults to Pearson correlation but can also be `bicor`.
`corOptions`	list specifying additional arguments to be passed to the correlation function given by `corFnc`.
`alternative`	alternative hypothesis for the correlation test
`qValues`	logical: should q-values be calculated?
`areaUnderROC`	logical: should are under the receiver-operating curve be calculated?

Details

The function calculates the correlations, associated p-values, area under the ROC, and q-values

Value

Data frame with the following components:

`ID`	Gene (or variable) identifiers copied from `colnames(datExpr)`
`cor`	correlations of all genes with the trait
`Z`	Fisher Z statistics corresponding to the correlations
`pvalueStudent`	Student p-values of the correlations
`qvalueStudent`	(if input `qValues==TRUE`) q-values of the correlations calculated from the p-values
`AreaUnderROC`	(if input `areaUnderROC==TRUE`) area under the ROC
`nPresentSamples`	number of samples present for the calculation of each association.

Author(s)

Steve Horvath