EvidenceAccumulation: Evidence accumulation clustering (co-association)

In MosaiClusteR: An Umbrella Framework for Multi-Source and Multi-Omics Clustering

Evidence accumulation clustering (co-association)

Description

EvidenceAccumulation builds a co-association (similarity) matrix from the individual source partitions and extracts a consensus partition by graph partitioning. Three graph-partitioning options are available: a minimum-spanning-tree procedure ("MST", requires the igraph package), a thresholded single-link procedure ("SL"), and an agnes single-link procedure ("SL_agnes").

Usage

EvidenceAccumulation(
  List,
  type = c("data", "dist", "clust"),
  distmeasure = c("tanimoto", "tanimoto"),
  normalize = c(FALSE, FALSE),
  method = c(NULL, NULL),
  clust = "agnes",
  linkage = c("flexible", "flexible"),
  alpha = 0.625,
  nrclusters = c(7, 7),
  gap = FALSE,
  maxK = 15,
  graphPartitioning = c("MST", "SL", "SL_agnes"),
  t = NULL
)

Arguments

List	A list of data matrices. It is assumed the rows correspond with the objects.
type	Indicates whether the provided matrices in "List" are either data matrices ("data"), distance matrices ("dist") or clustering results ("clust").
distmeasure	A vector of the distance measures to be used on each data matrix. Should be one of "tanimoto", "euclidean", "jaccard", "hamming". Defaults to c("tanimoto","tanimoto").
normalize	Logical. Indicates whether to normalize the distance matrices or not, defaults to c(FALSE, FALSE).
method	A method of normalization. Should be one of "Quantile","Fisher-Yates", "standardize","Range" or any of the first letters of these names. Default is c(NULL,NULL).
clust	Choice of clustering function (character). Defaults to "agnes".
linkage	Choice of inter group dissimilarity (character) for each data set. Defaults to c("flexible","flexible").
alpha	The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625.
nrclusters	The number of clusters to divide each individual dendrogram in. Default is c(7,7).
gap	Logical. Whether the optimal number of clusters should be determined with the gap statistic. Defaults to FALSE.
maxK	The maximal number of clusters to investigate in the gap statistic. Default is 15.
graphPartitioning	The graph partitioning method to be performed: "MST", "SL" or "SL_agnes".
t	A threshold used by the "MST" and "SL" partitioning procedures. Defaults to NULL.

Details

The co-association matrix S records the fraction of source partitions in which two objects are co-clustered. "MST" prunes a minimum spanning tree of the similarity graph (optionally at threshold t); "SL" performs a thresholded single-link grouping; and "SL_agnes" runs agnes single linkage on the dissimilarity 1-S. The "MST" path requires the suggested package igraph.

Value

A list of two elements:

DistM	A NULL object
Clust	The resulting clustering

The value has class 'Ensemble' (or 'Single Clustering' for "SL_agnes").

References

Fred A.L.N. and Jain A.K. (2005). Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 835-850.

Examples

## Not run: 
data(mosaic_toy)
L <- mosaic_toy$List
res <- EvidenceAccumulation(List = L, type = "data",
  distmeasure = c("euclidean","euclidean"), nrclusters = c(5,5),
  graphPartitioning = "SL_agnes")

## End(Not run)

MosaiClusteR documentation built on Oct. 25, 2026, 1:08 a.m.