This function blends standard and network approaches to selecting genes (or variables in general) with high gene significance
networkScreeningGS(
datExpr,
datME,
GS,
oddPower = 3,
blockSize = 1000,
minimumSampleSize = ..minNSamples,
addGS = TRUE)
weighted gene significance
copy of the input gene significances (only if addGS=TRUE)
data frame of expression data
data frame of module eigengenes
numeric vector of gene significances
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 gene significances be added to the screening statistics?
Steve Horvath
This function should be considered experimental. It takes into account both the "standard" and the network measures of gene importance for the trait.
networkScreening, automaticNetworkScreeningGS