2026/03/17 by Xiran Liu, Noah A. Rosenberg, Sohini Ramachandran · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Genetic Associations and Epidemiology #Genomics and Phylogenetic Studies
paper · pdf · doi:10.47248/hpgg2606020004
openalex publication_date 2026/03/17 · openalex created_date 2026/03/20 · openalex updated_date 2026/07/31
We previously introduced Clumppling to address the "alignment problem" for multiple mixed-membership unsupervised clustering results in population structure analyses, where clusters represent latent genetic ancestries. This problem stems from three challenges-label-switching, multi-modality, and varying numbers of clusters-which Clumppling resolves in three steps: aligning results with the same number of clusters, detecting distinct solutions or "modes," and aligning modes across different numbers of clusters. Here, we present Clumppling 2.0, an update with features for visualizing the emergence of clusters, comparing aligned results from different models, and incorporating modularity of algorithmic steps. We outline the Clumppling 2.0 workflow, highlighting its improved algorithmic flexibility and visual interpretability through a graph of alignment patterns. We then demonstrate its utility on human genetic datasets that include individuals from admixed populations.