coClustering.permutationTest | R Documentation |

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

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

`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 |

`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 |

`verbose` |
If non-zero, function will print out progress messages. |

`indent` |
Indentation for progress messages. Each unit adds two spaces. |

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.

`observed ` |
the observed co-clustering measures for clusters in |

`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). |

Peter Langfelder

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.

`coClustering`

for calculation of the "observed" co-clustering measure
`modulePreservation`

for a large suite of module preservation statistics

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

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