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.
Bibliographical noteFunding 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.
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