AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.

ABSTRACT: Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and

SUBMITTER: Eslami M 

PROVIDER: S-EPMC11479614 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature