wgcna.one.step {Pigengene} | R Documentation

## Module identification

### Description

This function is a wrapper function for `blockwiseModules` and passes its arguments to it. Some other arguments are fixed.

### Usage

    wgcna.one.step(Data, power, saveDir=".", blockSize = "All", saveTOMs = FALSE,
       doThreads=FALSE, verbose = 0, seed = NULL)

### Arguments

`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.
`power` | Soft-thresholding power for network construction
`saveDir` | The directory to save the results and plots. `NULL` will disable saving.
`blockSize` | The size of block when the data is too big. If not "All" (default) may introduce artifacts.
`saveTOMs` | Boolean determining if the TOM data should be saved, which can be hundreds of MBs and useful for identifying hubs.
`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.
`seed` | Random seed to ensure reproducibility.

### Details

`Data, power, blockSize, saveTOMs, verbose,` and `seed` are passd to `blockwiseModules`.

### Value

A list with following components

`call` | The command that created the results
`genes` | The names of `Data` columns
`modules` | A numeric vector, named by `genes`, that reports the module (clustering) assignments.
`moduleColors` | A character vector, named by `genes`, that reports the color of each gene according to its module assignment
`net` | The full output of `blockwiseModules` function
`netFile` | The file in which the net object is saved
`power` | An echo of the `power` argument.

### References

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

### See Also

`blockwiseModules`, `pickSoftThreshold`, `calculate.beta`

### Examples

         data(aml)
         data(mds)
         wgRes <- wgcna.one.step(Data=aml[,1:200], seed=1, power=7,
                                     saveDir="wgcna", verbose=1)

[Package Pigengene version 1.10.0 Index]