DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data.

ABSTRACT: Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types

SUBMITTER: Cheng L 

PROVIDER: S-EPMC9060369 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature