CytoPheno: Automated descriptive cell type naming in flow and mass cytometry.

ABSTRACT: Advances in cytometry have led to increases in the number of cellular markers that are routinely measured. The resulting complexity of the data has prompted a shift from manual to automated analysis methods. Currently, numerous unsupervised methods are available to cluster cells based on marker expression values. However, phenotyping the resulting clusters is typically not part of the automated process. Manually identifying both marker definitions (e.g. CD4 ⁺ , CCR7 ⁺ , CD45RA ⁺ , CD19 ^- ) and descriptive cell type names (e.g. naïve CD4 ⁺ T cells) based on marker expression values can be time-consuming, subjective, and error-prone. In this work we propose an algorithm that addresses these problems through the creation of an automated t

SUBMITTER: Tursi AR 

PROVIDER: S-EPMC11952469 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature