SingletSeeker: An unsupervised clustering approach for automated singlet discrimination in cytometry.

ABSTRACT: Flow cytometry is a high-throughput, high-dimensional technique that generates large sets of single-cell data. Prior to analyzing this data, it is common to exclude any events that contain two or more cells, multiplets, to ensure downstream analysis and quantification is of single-cell events, singlets, only. The process of singlet discrimination is critical yet fundamentally subjective and time-consuming; it is performed manually by the user, where the proper exclusion of multiplets depends on the user's expertise and often varies from experiment to experiment. To address this problem, we have developed an algorithm to automatically discriminate singlets from other unwanted events such as multiplets and debris. Using parameters derived from imaging, the algorithm first identifies high-den

SUBMITTER: Colasurdo M 

PROVIDER: S-EPMC12103632 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature