Consensus Clustering for Different Sample Coverage Data
Description
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).