WGCNA: coClustering.permutationTest – R documentation

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coClustering.permutationTest

Permutation test for co-clustering

Description

This function calculates permutation Z statistics that measure how different the co-clustering of modules in a reference and test clusterings is from random.

Usage

coClustering.permutationTest(
      clusters.ref, clusters.test, 
      tupletSize = 2, 
      nPermutations = 100, 
      unassignedLabel = 0, 
      randomSeed = 12345, verbose = 0, indent = 0)

Arguments

`clusters.ref`	Reference input clustering. A vector in which each element gives the cluster label of an object.
`clusters.test`	Test input clustering. Must be a vector of the same size as `cluster.ref`.
`tupletSize`	Co-clutering tuplet size.
`nPermutations`	Number of permutations to execute. Since the function calculates parametric p-values, a relatively small number of permutations (at least 50) should be sufficient.
`unassignedLabel`	Optional specification of a clustering label that denotes unassigned objects. Objects with this label are excluded from the calculation.
`randomSeed`	Random seed for initializing the random number generator. If `NULL`, the generator is not initialized (useful for calling the function sequentially). The default assures reproducibility.
`verbose`	If non-zero, function will print out progress messages.
`indent`	Indentation for progress messages. Each unit adds two spaces.

Details

This function performs a permutation test to determine whether observed co-clustering statistics are significantly different from those expected by chance. It returns the observed co-clustering as well as the permutation Z statistic, calculated as (observed - mean)/sd, where mean and sd are the mean and standard deviation of the co-clustering when the test clustering is repeatedly randomly permuted.

Value

`observed`	the observed co-clustering measures for clusters in `clusters.ref`
`Z`	permutation Z statics
`permuted.mean`	means of the co-clustering measures when the test clustering is permuted
`permuted.sd`	standard deviations of the co-clustering measures when the test clustering is permuted
`permuted.cc`	values of the co-clustering measure for each permutation of the test clustering. A matrix of dimensions (number of permutations)x(number of clusters in reference clustering).

Author(s)

Peter Langfelder

References

For example, see Langfelder P, Luo R, Oldham MC, Horvath S (2011) Is My Network Module Preserved and Reproducible? PLoS Comput Biol 7(1): e1001057. Co-clustering is discussed in the Methods Supplement (Supplementary text 1) of that article.

Examples

set.seed(1);
  nModules = 5;
  nGenes = 100;
  cl1 = sample(c(1:nModules), nGenes, replace = TRUE);
  cl2 = sample(c(1:nModules), nGenes, replace = TRUE);
  
  cc = coClustering(cl1, cl2)

  # Choose a low number of permutations to make the example fast
  ccPerm = coClustering.permutationTest(cl1, cl2, nPermutations = 20, verbose = 1);

  ccPerm$observed
  ccPerm$Z

  # Combine cl1 and cl2 to obtain clustering that is somewhat similar to cl1:

  cl3 = cl2;
  from1 = sample(c(TRUE, FALSE), nGenes, replace = TRUE);
  cl3[from1] = cl1[from1];

  ccPerm = coClustering.permutationTest(cl1, cl3, nPermutations = 20, verbose = 1);

  # observed co-clustering is higher than before:
  ccPerm$observed

  # Note the high preservation Z statistics:
  ccPerm$Z

WGCNA

Weighted Correlation Network Analysis

v1.70-3

GPL (>= 2)

Authors

Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang

Initial release

2021-02-17