Clumppling: cluster matching and permutation program with integer linear programming

Abstract

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 frameworks. In example analyses, we find that it achieves solutions with preferred values of a desired objective function relative to those achieved by Pong and that it proceeds with less computation time than Clumpak. It is also the first method to permit alignments across replicates with multiple arbitrary values of the number of clusters K. Availability and implementation Clumppling is available at https://github.com/PopGenClustering/Clumppling.