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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 |
| Author: | Chuanxing Li [aut, cre], Meng Zhou [aut] |
| Maintainer: | Chuanxing Li <chuan-xing.li at ki.se> |
| 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: ccml_1.4.0.zip, r-release: ccml_1.4.0.zip, r-oldrel: ccml_1.4.0.zip |
| 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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