calculate.beta: Estimates an appropriate power value

In Pigengene: Infers biological signatures from gene expression data

Description

The WGCNA package assumes that in the coexpression network the genes are connected with a power-law distribution. Therefore, it need a soft-thresholding power for network construction, which is estimated by this auxiliary function.

Usage

calculate.beta(saveFile = NULL, RsquaredCut = 0.8, Data,  doThreads=FALSE, 
  verbose = 0)

Arguments

saveFile	The file to save the results in. Set to NULL to disable.
RsquaredCut	A threshold in the range [0,1] used to estimate the power. A higher value can increase power. For technical use only. See pickSoftThreshold for more details.
Data	A matrix or data frame containing the expression data, with genes corresponding to columns and rows corresponding to samples. Rows and columns must be named.
doThreads	Boolean. Allows WGCNA to run a little faster using multi-threading but might not work on all systems.
verbose	The integer level of verbosity. 0 means silent and higher values produce more details of computation.

Value

A list of:

sft	The full output of pickSoftThreshold function
power	The estimated power (beta) value
powers	The numeric vector of all tried powers
RsquaredCut	The value of input argument RsquaredCut

References

Langfelder P and Horvath S, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559

See Also

pickSoftThreshold, blockwiseModules, one.step.pigengene, wgcna.one.step

Examples

     data(aml)
     p1 <- calculate.beta(Data=aml[,1:200])

Pigengene documentation built on Nov. 8, 2020, 6:47 p.m.