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

ABSTRACT: 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

SUBMITTER: Anibal J 

PROVIDER: S-EPMC9560626 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature