CyAnno: a semi-automated approach for cell type annotation of mass cytometry datasets.

ABSTRACT: For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. 'ungated' cells, and that current semi-automated approaches ignore their modeling resulting in misclassified annotations. We introduce 'CyAnno', a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of 'gated' cell types and the 'ungated' cells. By applying this framework on several CyTOF datasets, we demonstrated that including the 'ungated' cells can lead to

SUBMITTER: Kaushik A 

PROVIDER: S-EPMC9502137 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature