Prediction of functional markers of mass cytometry data via deep learning

Solís-Lemus, C., Ma, X., HostetterII, M.

Recently, there has been an increasing interest in the analysis of flow cytometry data, which involves measurements of a set of surface and functional markers across hundreds and thousands of cells. These measurements can often be used to differentiate various cell types and there has been a rapid development of analytic approaches for achieving this. However, in spite of the fact that measurements are available on such a large number of cells, there have been very limited advances in deep learning approaches for the analysis of flow cytometry data. Some preliminary work has focused on using deep learning techniques to classify cell types based on the cell protein measurements. In a first of its kind study, we propose a novel deep learning architecture for predicting functional markers in the cells given data on surface markers. Such an approach is expected to automate the measurement of functional markers across cell samples, provided data on the surface markers are available, that has important practical advantages. We validate and compare our approach with competing machine learning methods using a real flow cytometry dataset, and showcase the improved prediction performance of the deep learning architecture.

Single-cell genomics, Single-cell chromatin accessibility data, Rare cell types, Weighted K-means clustering, Sparse weighted K-means clustering 


Solís-Lemus, C., Ma, X., HostetterII, M. "Prediction of functional markers of mass cytometry data via deep learning" Statistical Modeling in Biomedical Research (2021): 95-104