ccml: Consensus Clustering for Different Sample Coverage Data

Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly used in clinical data. Current consensus clustering methods tend to ensemble a number of different clusters from mathematical replicates with similar sample coverage. As the fact of common variety of sample coverage in the real-world data, a new consensus clustering strategy dealing with such biological replicates is required. This is a two-step consensus clustering package, which is used to input multiple predictive labels with different sample coverage (missing labels).

Version: 1.4.0
Depends: R (≥ 3.5.0)
Imports: ggplot2, diceR, parallel, tidyr, SNFtool, plyr, ConsensusClusterPlus (≥ 1.56.0)
Suggests: spelling, testthat (≥ 3.0.0)
Published: 2023-08-30
DOI: 10.32614/CRAN.package.ccml
Author: Chuanxing Li [aut, cre], Meng Zhou [aut]
Maintainer: Chuanxing Li < at>
License: GPL-2
NeedsCompilation: no
Language: en-US
Materials: NEWS
CRAN checks: ccml results


Reference manual: ccml.pdf


Package source: ccml_1.4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ccml_1.4.0.tgz, r-oldrel (arm64): ccml_1.4.0.tgz, r-release (x86_64): ccml_1.4.0.tgz, r-oldrel (x86_64): ccml_1.4.0.tgz
Old sources: ccml archive


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