Description Usage Arguments Details Value References See Also Examples

This function implements the consensus cluster algorithm.

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`dat` |
Probe by sample omic data matrix. Data should be filtered and normalized prior to analysis. |

`max_k` |
Integer specifying the maximum cluster number to evaluate.
Default is |

`reps` |
Number of subsamples to draw. |

`distance` |
Distance metric for clustering. Supports all methods
available in |

`cluster_alg` |
Clustering algorithm to implement. Currently supports
hierarchical ( |

`hclust_method` |
Method to use if |

`p_item` |
Proportion of items to include in each subsample. |

`p_feature` |
Proportion of features to include in each subsample. |

`wts_item` |
Optional vector of item weights. |

`wts_feature` |
Optional vector of feature weights. |

`seed` |
Optional seed for reproducibility. |

`parallel` |
If a parallel backend is loaded and available, should the function use it? Highly advisable if hardware permits. |

`check` |
Check for errors in function arguments? This is set to |

Consensus clustering is a resampling procedure to evaluate cluster stability.
A user-specified proportion of samples are held out on each run of the
algorithm to test how often the remaining samples do or do not cluster
together. The result is a square, symmetric consensus matrix for each value
of cluster numbers *k*. Each cell of the matrix `mat[i, j]`

represents the proportion of all runs including samples `i`

and `j`

in which the two were clustered together.

A list with `max_k`

elements, the first of which is `NULL`

.
Elements two through `max_k`

are consensus matrices corresponding to
cluster numbers *k* = 2 through `max_k`

.

Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003).
Consensus
Clustering: A Resampling-Based Method for Class Discovery and Visualization
of Gene Expression Microarray Data. *Machine Learning*, *52*:
91-118.

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