WGCNA: networkScreening – R documentation

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networkScreening

Identification of genes related to a trait

Description

This function blends standard and network approaches to selecting genes (or variables in general) highly related to a given trait.

Usage

networkScreening(y, datME, datExpr, 
                 corFnc = "cor", corOptions = "use = 'p'",
                 oddPower = 3, 
                 blockSize = 1000, 
                 minimumSampleSize = ..minNSamples,
                 addMEy = TRUE, removeDiag = FALSE, 
                 weightESy = 0.5, getQValues = TRUE)

Arguments

`y`	clinical trait given as a numeric vector (one value per sample)
`datME`	data frame of module eigengenes
`datExpr`	data frame of expression data
`corFnc`	character string specifying the function to be used to calculate co-expression similarity. Defaults to Pearson correlation. Any function returning values between -1 and 1 can be used.
`corOptions`	character string specifying additional arguments to be passed to the function given by `corFnc`. Use `"use = 'p', method = 'spearman'"` to obtain Spearman correlation.
`oddPower`	odd integer used as a power to raise module memberships and significances
`blockSize`	block size to use for calculations with large data sets
`minimumSampleSize`	minimum acceptable number of samples. Defaults to the default minimum number of samples used throughout the WGCNA package, currently 4.
`addMEy`	logical: should the trait be used as an additional "module eigengene"?
`removeDiag`	logical: remove the diagonal?
`weightESy`	weight to use for the trait as an additional eigengene; should be between 0 and 1
`getQValues`	logical: should q-values be calculated?

Details

This function should be considered experimental. It takes into account both the "standard" and the network measures of gene importance for the trait.

Value

datout = data.frame(p.Weighted, q.Weighted, Cor.Weighted, Z.Weighted, p.Standard, q.Standard, Cor.Standard, Z.Standard) Data frame reporting the following quantities for each given gene:

`p.Weighted`	weighted p-value of association with the trait
`q.Weighted`	q-value (local FDR) calculated from `p.Weighted`
`cor.Weighted`	correlation of trait with gene expression weighted by a network term
`Z.Weighted`	Fisher Z score of the weighted correlation
`p.Standard`	standard Student p-value of association of the gene with the trait
`q.Standard`	q-value (local FDR) calculated from `p.Standard`
`cor.Standard`	correlation of gene with the trait
`Z.Standard`	Fisher Z score of the standard correlation

Author(s)

Steve Horvath

WGCNA

Weighted Correlation Network Analysis

v1.70-3

GPL (>= 2)

Authors

Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang

Initial release

2021-02-17