Clumppling: cluster matching and permutation program with integer linear programming.

ABSTRACT:

Motivation

In the mixed-membership unsupervised clustering analyses commonly used in population genetics, multiple replicate data analyses can differ in their clustering solutions. Combinatorial algorithms assist in aligning clustering outputs from multiple replicates so that clustering solutions can be interpreted and combined across replicates. Although several algorithms have been introduced, challenges exist in achieving optimal alignments and performing alignments in reasonable computation time.

Results

We present Clumppling, a method for aligning replicate solutions in mixed-membership unsupervised clustering. The method uses integer linear programming for finding optimal alignments, embedding the cluster alignment problem in standard combinatorial optimization framewo

SUBMITTER: Liu X 

PROVIDER: S-EPMC10766593 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature