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Mining High-Dimensional CyTOF Data: Concurrent Gating, Outlier Removal, and Dimension Reduction

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Databases Theory and Applications (ADC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10538))

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Abstract

Cytometry is a powerful tool in clinical diagnosis of health disorders, in particular, immunodeficiency diseases and acute leukemia. Recent technological advancements have enabled up to 100 measurements to be taken simultaneously on each cell, thus generating high-throughput and high-dimensional datasets. Current analysis, relying on manual segmentation of cell populations (gating) on sequential low-dimensional projections of the data, is subjective, time consuming and error-prone. It is also known that these multidimensional cytometric data typically exhibit non-normal features, including asymmetry, multimodality, and heavy tails. This present a great challenge to traditional clustering methods which are typically based on symmetric distributions.

In recent years, non-normal distributions have received increasing interest in the statistics literature. In particular, finite mixtures of skew distributions have emerged as a promising alternative to the traditional normal mixture modelling. However, these models are not well suited to high-dimensional settings.

This paper describes a flexible statistical approach designed for performing, at the same time, unsupervised clustering, dimension reduction, and outlier removal for cytometric data. The approach is based on finite mixtures of multivariate skew normal factor analyzers (SkewFA) with threshold pruning. The model can be fitted by maximum likelihood (ML) via an expectation-maximization (EM) algorithm. An application to a large CyTOF data is presented to demonstrate the usefulness of the SkewFA model and to illustrate its effectiveness relative to other algorithms.

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Authors and Affiliations

  1. School of Mathematics and Physics, University of Queensland, Brisbane, Australia

    Sharon X. Lee

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  1. Sharon X. Lee
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Correspondence to Sharon X. Lee .

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Editors and Affiliations

  1. University of Queensland, Brisbane, Queensland, Australia

    Zi Huang

  2. Nanyang Technological University, Singapore, Singapore

    Xiaokui Xiao

  3. University of New South Wales, Sydney, New South Wales, Australia

    Xin Cao

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Lee, S.X. (2017). Mining High-Dimensional CyTOF Data: Concurrent Gating, Outlier Removal, and Dimension Reduction. In: Huang, Z., Xiao, X., Cao, X. (eds) Databases Theory and Applications. ADC 2017. Lecture Notes in Computer Science(), vol 10538. Springer, Cham. https://doi.org/10.1007/978-3-319-68155-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-68155-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68154-2

  • Online ISBN: 978-3-319-68155-9

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