calculate.beta {Pigengene} | R Documentation

## Estimates an appropriate power value

### Description

`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`, `WGCNA-package`, `one.step.pigengene`, `wgcna.one.step`

### Examples

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

[Package Pigengene version 1.8.1 Index]