Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aghaeepour, N., Nikolic, R., Hoos, H. H., & Brinkman, R. R. (2010). Rapid cell population identification in flow cytometry data. Cytometry Part A, 79A(1), 6–13.
Van Gassen, S., Callebaut, B., Van Helden, M. J., Lambrecht, B. N., Demeester, P., Dhaene, T., et al. (2015). FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry Part A, 87(7), 636–645.
Samusik, N., Good, Z., Spitzer, M. H., Davis, K. L., & Nolan, G. P. (2016). Nolan. Automated mapping of phenotype space with single-cell data. Nature Methods, 13, 493.
Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs Jr, K. D., Bruggner, R. V., Linderman, M. D., et al. (2011). Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nature Biotechnology, 29, 886.
Maaten, L. V. D., & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
Spitzer, M. H., Gherardini, P. F., Fragiadakis, G. K., Bhattacharya, N., Yuan, R. T., Hotson, A. N., et al. (2015). An interactive reference framework for modeling a dynamic immune system. Science, 349(6244), 2015.
Cire ̧san, D., Giusti, A., Gambardella, L., & Schmidhuber, J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. In Mori, K.Sakuma, I, Sato, Y., Barillot, C., & Navab, N. (Eds.), Medical image computing and computer-assisted intervention. Berlin: Springer.
O. Denas, O., & J. Taylor (2013). Deep modeling of gene expression regulation in an Erythropoiesis model. In Representation Learning, ICML Workshop New York: ACM.
Fakoor, R., Ladhak, F., Nazi, A., & Huber, M. (2013). Using deep learning to enhance cancer diagnosis and classification. In Proceedings of the 30th International Conference on Machine Learning (ICML). New York: ACM.
Leung, M. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12), i121–i129.
Cruz-Roa, A., Basavanhally, A., González, F., Gilmore, H., Feldman, M., Ganesan, S., .et al. (2014). Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In SPIE Medical Imaging (Vol. 9041, pp. 904103).
Li, H., Shaham, U., Stanton, K. P., Yao, Y., Montgomery, R. R., & Kluger, Y. (2017). Gating mass cytometry data by deep learning. Bioinformatics, 33(21), 3423–3430.
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Vega, J. E. V., et al. (2018). Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences, 115(13), E2970–E2979
Bendall, S. C., Simonds, E. F., Qiu, P., El-ad, D. A., Krutzik, P. O., Finck, R., et al. (2011). Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science, 332(6030), 687–696.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. Preprint. arXiv:1412.6980.
Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. (2017). Julia: A Fresh Approach to Numerical Computing. SIAM Review, 59(1), 65–98.
Mocha: Julia package. https://mochajl.readthedocs.io/en/latest/. Retrieved October 22, 2018.
ScikitLearn: Julia package. https://scikitlearnjl.readthedocs.io/en/latest/. Retrieved October 22, 2018.
Smola, A., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222.
Welling, M. (2004). Support vector regression. Tech. Rep., Department of Computer Science, Univ. of Toronto.
Acknowledgement
We thank the editor as well as two anonymous reviewers whose suggestions greatly improved the manuscript.
Data Sharing
The data is publicly available through the original publications [4, 14] in the following website: http://reports.cytobank.org/1/v1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Solís-Lemus, C., Ma, X., Hostetter, M., Kundu, S., Qiu, P., Pimentel-Alarcón, D. (2020). Prediction of Functional Markers of Mass Cytometry Data via Deep Learning. In: Zhao, Y., Chen, DG. (eds) Statistical Modeling in Biomedical Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-33416-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-33416-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33415-4
Online ISBN: 978-3-030-33416-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)