This function blends standard and network approaches to selecting genes (or variables in general) highly related to a given trait.
networkScreening(y, datME, datExpr,
corFnc = "cor", corOptions = "use = 'p'",
oddPower = 3,
blockSize = 1000,
minimumSampleSize = ..minNSamples,
addMEy = TRUE, removeDiag = FALSE,
weightESy = 0.5, getQValues = TRUE)clinical trait given as a numeric vector (one value per sample)
data frame of module eigengenes
data frame of expression data
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.
character string specifying additional arguments to be passed to the function given
by corFnc. Use "use = 'p', method = 'spearman'" to obtain Spearman correlation.
odd integer used as a power to raise module memberships and significances
block size to use for calculations with large data sets
minimum acceptable number of samples. Defaults to the default minimum number of samples used throughout the WGCNA package, currently 4.
logical: should the trait be used as an additional "module eigengene"?
logical: remove the diagonal?
weight to use for the trait as an additional eigengene; should be between 0 and 1
logical: should q-values be calculated?
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:
weighted p-value of association with the trait
q-value (local FDR) calculated from p.Weighted
correlation of trait with gene expression weighted by a network term
Fisher Z score of the weighted correlation
standard Student p-value of association of the gene with the trait
q-value (local FDR) calculated from p.Standard
correlation of gene with the trait
Fisher Z score of the standard correlation
This function should be considered experimental. It takes into account both the "standard" and the network measures of gene importance for the trait.