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

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. Therefore, 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 a significant increase in the prediction accuracy of the gated cell types. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.