Gating mass cytometry data by deep learning
2017
Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single
cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New
methods for analyzing CyTOF data attempt to improve automation, scalability, performance, and
interpretation of data generated in large studies. Assigning individual cells into discrete groups of
cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies.
We introduce DeepCyTOF, a standardization approach for gating, based on deep learning
techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain
adaptation principles and is a generalization of previous work that allows us to calibrate between
a target distribution and a source distribution in an unsupervised manner. We show that Deep-
CyTOF is highly concordant (98%) with cell classification obtained by individual manual gating
of each sample when applied to a collection of 16 biological replicates of primary immune blood
cells, even when measured accross several instruments. Further, DeepCyTOF achieves very high
accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two
CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection
with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that
deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach
for semi-automated gating of CyTOF and flow cytometry data.
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