Gating mass cytometry data by deep learning

Huamin Li, Uri Shaham, Kelly P. Stanton, Yi Yao, Ruth R. Montgomery, Yuval Kluger

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

Motivation: 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.

Results: 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 DeepCyTOF 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 across 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.

Availability and implementation: Our codes and data are publicly available at https://github.com/KlugerLab/deepcytof.git.

Contact: [email protected].

Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)3423-3430
Number of pages8
JournalBioinformatics
Volume33
Issue number21
DOIs
StatePublished - 1 Nov 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected]

Funding

FundersFunder number
National Human Genome Research InstituteR01HG008383

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