Gating mass cytometry data by deep learning

BioRxiv : the Preprint Server for Biology
Huamin LiYuval Kluger

Abstract

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 generat...Continue Reading

Related Concepts

Single-Cell Analysis
Study
Flow Cytometry
Classification
2-Dimensional
Adaptation
Instrument - Device
West Nile Virus Pathway
Cell Type
Learning

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

BioRxiv : the Preprint Server for Biology
Tamim AbdelaalAhmed Mahfouz
American Journal of Transplantation : Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons
S M KramsO M Martinez
© 2020 Meta ULC. All rights reserved