Model-based cell clustering and population tracking for time-series flow cytometry data.

ABSTRACT: BACKGROUND:Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on "manual gating" by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis. Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data. RESULTS:We propose a simple and efficient sta

SUBMITTER: Minoura K 

PROVIDER: S-EPMC6933651 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature