<i>Clumppling 2.0</i>: A Clustering Alignment Program for Population Structure Analyses

Xiran Liu; Noah A. Rosenberg; Sohini Ramachandran

doi:10.47248/hpgg2606020004

Human Population Genetics and Genomics 2026;6(2):0004 | https://doi.org/10.47248/hpgg2606020004

Technical Note Open Access

Clumppling 2.0: A Clustering Alignment Program for Population Structure Analyses

Xiran Liu ¹ , Noah A. Rosenberg ² , Sohini Ramachandran ^1,3

1. Data Science Institute, Brown University, Providence, RI, 02912, USA
2. Department of Biology, Stanford University, Stanford, CA, 94305, USA
3. Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI, 02912, USA

Correspondence: Xiran Liu

Academic Editor(s): Joshua Akey, Carina Schlebusch, Torsten Günther

Received: Nov 21, 2025 | Accepted: Feb 24, 2026 | Published: Mar 17, 2026

This article belongs to the Special Issue Population Genetics Methods and Software

Cite this article: Liu X, Rosenberg NA, Ramachandran S. Clumppling 2.0: A Clustering Alignment Program for Population Structure Analyses. Hum Popul Genet Genom. 2026;6(2):0004. https://doi.org/10.47248/hpgg2606020004

Abstract

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.

Keywords

admixture, alignment, clustering, genetic ancestry, population structure

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