Gating mass cytometry data by deep learning
| Autor: | Kelly P Stanton, Huamin Li, Yi Yao, Yuval Kluger, Uri Shaham, Ruth R. Montgomery |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Statistics and Probability Cell type Computer science Single sample Cell Separation 02 engineering and technology Gating Biology Bioinformatics Biochemistry Flow cytometry Machine Learning 03 medical and health sciences 0302 clinical medicine Single-cell analysis 0202 electrical engineering electronic engineering information engineering medicine Humans Mass cytometry Molecular Biology 030304 developmental biology 0303 health sciences Blood Cells medicine.diagnostic_test business.industry Deep learning Healthy subjects Computational Biology Reproducibility of Results Pattern recognition Reference Standards Flow Cytometry Original Papers Automation Computer Science Applications Computational Mathematics 030104 developmental biology Computational Theory and Mathematics 030220 oncology & carcinogenesis Calibration Scalability 020201 artificial intelligence & image processing Artificial intelligence Single-Cell Analysis business |
| Zdroj: | Bioinformatics. 33:3423-3430 |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | 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. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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