Fast tree aggregation for consensus hierarchical clustering.

ABSTRACT:

Background

In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues.

Results

We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same clust

SUBMITTER: Hulot A 

PROVIDER: S-EPMC7085155 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature