coClustering | R Documentation |

The function calculates the co-clustering statistics for each module in the reference clustering.

coClustering(clusters.ref, clusters.test, tupletSize = 2, unassignedLabel = 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. |

`unassignedLabel` |
Optional specification of a clustering label that denotes unassigned objects. Objects with this label are excluded from the calculation. |

Co-clustering of cluster q in the reference clustering and cluster q' in the test clustering measures the overlap of clusters q and q' by the number of tuplets that can be chosen from the overlap of clusters q and q' relative to the number of tuplets in cluster q. To arrive at a co-clustering measure for cluster q, we sum the co-clustering of q and q' over all clusters q' in the test clustering. A value close to 1 indicates high preservation of the reference cluster in the test clustering, while a value close to zero indicates a low preservation.

A vector in which each component corresponds to a cluster in the reference clustering. Entries give the co-clustering measure of cluster preservation.

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.

`modulePreservation`

for a large suite of module preservation statistics
`coClustering.permutationTest`

for a permutation test for co-clustering significance

# An example with random (unrelated) clusters: set.seed(1); nModules = 10; nGenes = 1000; cl1 = sample(c(1:nModules), nGenes, replace = TRUE); cl2 = sample(c(1:nModules), nGenes, replace = TRUE); coClustering(cl1, cl2) # For the same reference and test clustering: coClustering(cl1, cl1)

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