Abstract
Cytometry is frequently used in immunological research, pre-clinical trials, clinical diagnosis, and monitoring of lymphomas, leukemia, and AIDS. However, analysis of modern high-throughput cytometric data presents great challenges for current computational tools due to the high dimensionality, large number of observations, as well as complex distributional features such as multimodality, asymmetry, and other non-normal characteristics. This chapter proposes a novel statistical approach that can automatically cluster and perform implicit dimension reduction of high-dimensional cytometry data. Our approach is also robust against non-normal distributional features such as heterogeneity and skewness that are typical in flow and mass cytometry data. By adopting a factor analytic model-based approach, the proposed framework is able to learn latent nonlinear low-dimensional representations of the data. It thus allows automatic segmentation of cell populations and quantification of the relative importance of each markers, while also facilitating visualization in low-dimensional space. The effectiveness of our approach is demonstrated on a large mass cytometry data, outperforming existing benchmark algorithms.
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Lee, S.X., McLachlan, G.J., Pyne, S. (2021). Automated Gating and Dimension Reduction of High-Dimensional Cytometry Data. In: Molina-París, C., Lythe, G. (eds) Mathematical, Computational and Experimental T Cell Immunology. Springer, Cham. https://doi.org/10.1007/978-3-030-57204-4_16
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