HAL-X: Scalable hierarchical clustering for rapid and tunable single-cell analysis

James Anibal, Alexandre G. Day, Erol Bahadiroglu, Liam O'Neil, Long Phan, Alec Peltekian, Amir Erez, Mariana Kaplan, Grégoire Altan-Bonnet*, Pankaj Mehta*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner.

Original languageAmerican English
Article number10
Pages (from-to)1010349
JournalPLoS Computational Biology
Issue number10
StatePublished - Oct 2022
Externally publishedYes

Bibliographical note

Funding Information:
PM and AGD were supported by Simons Investigator in the Mathematical Modelling of Living Systems Grant, and NIH Grant No. R35GM119461. This research was supported in part by the Intramural Research Program of the NIH (MK and GA-B research groups). This project was initiated with a seed grant to GA-B and PM from the Gordon and Betty Moore foundation and Research Corporation through the Scialog program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2022 Public Library of Science. All rights reserved.


Dive into the research topics of 'HAL-X: Scalable hierarchical clustering for rapid and tunable single-cell analysis'. Together they form a unique fingerprint.

Cite this