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Description

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).
Metadata

Version

1.4.0

License

Unknown

Platforms (75)

    Darwin
    FreeBSD
    Genode
    GHCJS
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    MMIXware
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    none
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