networkScreeningGS | R Documentation |
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)
datExpr |
data frame of expression data |
datME |
data frame of module eigengenes |
GS |
numeric vector of gene significances |
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. |
addGS |
logical: should gene significances be added to the screening statistics? |
This function should be considered experimental. It takes into account both the "standard" and the network measures of gene importance for the trait.
GS.Weighted |
weighted gene significance |
GS |
copy of the input gene significances (only if |
Steve Horvath
networkScreening
, automaticNetworkScreeningGS
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