M_ABCdist: Multi-source M-ABC with distance accumulation

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

Multi-source M-ABC with distance accumulation

Description

Runs ABCdist.SingleInMultiple on each source to obtain a [0, 1] dissimilarity, combines them as a weighted mean D = \sum_k w_k D_k and applies a final agglomerative clustering.

Usage

M_ABCdist(
  List,
  source_weights = NULL,
  distmeasure = rep("euclidean", length(List)),
  weighting = rep("var", length(List)),
  normalize = rep(list(NULL), length(List)),
  gr = c(),
  bag = rep(TRUE, length(List)),
  numsim = 1000L,
  numvar = rep(100L, length(List)),
  linkage = rep("ward.D", length(List)),
  NC = NULL,
  NC2 = NULL,
  final_linkage = "ward",
  alpha = 0.625,
  mds = FALSE,
  nfeat = NULL,
  nugget_type = "between",
  nugget_args = list()
)

Arguments

List	A list of K data matrices over the same objects, objects (samples) in rows and features in columns.
source_weights	Optional non-negative source weights (normalised to sum to one); NULL = equal weights.
distmeasure, weighting, normalize, bag, numvar, linkage	Per-source vectors (recycled to K) forwarded to ABCpp.SingleInMultiple.
gr	Unused; kept for compatibility.
numsim	Iterations per source.
NC	Base-cluster count per source (NULL = data-driven).
NC2	Optional number of clusters to cut the final tree into.
final_linkage, alpha	Final clustering controls.
mds	Logical; MDS of the consensus dissimilarities.
nfeat	Feature-subsample size forwarded to ABCpp.SingleInMultiple (NULL = \lfloor\sqrt{G}\rfloor).
nugget_type, nugget_args	Forwarded to the nugget weighting.

Value

A list of class "M_ABCdist" with source_D, DistM, weights and Clust.

References

\insertCite

Amaratunga2008MosaiClusteR

See Also

M_ABC, ABCdist.SingleInMultiple, M_ABCdist.WC

Examples

data(mosaic_toy)
fit <- M_ABCdist(mosaic_toy$List, numsim = 40, weighting = c("var", "nugget"))
dim(fit$DistM)

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