WGCNA: networkScreeningGS – R documentation

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networkScreeningGS

Network gene screening with an external gene significance measure

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 `addGS=TRUE`)

Steve Horvath