standardScreeningNumericTrait: Standard screening for numeric traits

In WGCNA: Weighted Correlation Network Analysis

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

See Also

standardScreeningBinaryTrait, standardScreeningCensoredTime

WGCNA documentation built on Sept. 18, 2024, 5:08 p.m.