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| CyGate is a semiautomated method for classifying single cells into their respective cell types. CyGate learns a gating strategy from a reference data set, trains a model for cell classification, and then automatically analyzes additional data sets using the trained model. CyGate also supports the machine learning framework for the classification of "ungated" cells, which are typically disregarded by automated methods. CyGate's utility was demonstrated by its high performance in cell type classification and the lowest generalization error on various public data sets when compared to the state-of-the-art semiautomated methods. Notably, CyGate had the shortest execution time, allowing it to scale with a growing number of samples. |
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 Publication
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CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data.
Seungjin Na, Yujin Choo, Tae Hyun Yoon, and Eunok Paek.
Analytical Chemistry, 2023, 95(46), 16918-16926. [ PMID: 37946317 ] |
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