ABCpp.SingleInMultiple: Single-source Aggregating Bundles of Clusters (ABC),...

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

Single-source Aggregating Bundles of Clusters (ABC), C++-accelerated engine

Description

The canonical single-source ABC engine of \insertCiteAmaratunga2008MosaiClusteR. Over numsim iterations it (optionally) bootstraps the objects, draws a weighted subsample of features, clusters the sub-matrix with fastcluster::hclust, and records each selected object's base-cluster label. M_ABC turns the stacked label matrix into a consensus dissimilarity using the C++ co-clustering kernel.

Usage

ABCpp.SingleInMultiple(
  data,
  distmeasure = "euclidean",
  weighting = "var",
  normalize = NULL,
  gr = c(),
  bag = TRUE,
  numsim = 1000,
  numvar = 100,
  linkage = "ward.D2",
  NC = NULL,
  mds = FALSE,
  nfeat = NULL,
  nugget_type = "between",
  nugget_args = list()
)

Arguments

data	Numeric matrix with objects (samples) in rows and features in columns (the MosaiClusteR convention; transposed internally to the features-by-objects layout of the original engine).
distmeasure	Distance measure passed to Distance.
weighting	"var", "cv", "nugget" or equal.
normalize	Optional normalisation (see Normalization).
gr	Unused; kept for interface compatibility.
bag	Logical; bootstrap the objects each iteration (Step 2).
numsim	Number of iterations R.
numvar	Kept for backward compatibility; superseded by nfeat.
linkage	Agglomeration method for fastcluster::hclust.
NC	Base-cluster count (NULL = \lfloor\sqrt{N}\rfloor).
mds	Unused placeholder.
nfeat	Optional feature-subsample size. NULL (default) uses the classic g=\lfloor\sqrt{G}\rfloor; a value \ge 1 sets an absolute count (raise it for low-dimensional sources where most features matter).
nugget_type, nugget_args	Passed to the data-nugget weighting when weighting = "nugget".

Value

A data frame, numsim rows by one column per object; entries are base-cluster labels (0 = object not selected that iteration).

Feature weighting (incl. data nuggets)

Features are subsampled with probability proportional to a rank-transformed importance score:

• "var" – feature variance (the classic ABC choice);

• "cv" – coefficient of variation;

• "nugget" – NEW: the data-nugget between-group weighted variance (create_data_nuggets), a robust, Big-Data-friendly alternative to the raw variance;

• anything else – equal weights.

References

\insertAllCited

See Also

M_ABC, create_data_nuggets

Examples

data(mosaic_toy)
lab <- ABCpp.SingleInMultiple(mosaic_toy$List[[1]], numsim = 40, NC = 3,
                              weighting = "nugget")
dim(lab)

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