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Computational Analysis of High-Dimensional Flow Cytometric Data for Diagnosis and Discovery

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High-Dimensional Single Cell Analysis

Part of the book series: Current Topics in Microbiology and Immunology ((CT MICROBIOLOGY,volume 377))

Abstract

Recent technological advancements have enabled the flow cytometric measurement of tens of parameters on millions of cells. Conventional manual data analysis and bioinformatics tools cannot provide a complete analysis of these datasets due to this complexity. In this chapter we will provide an overview of a general data analysis pipeline both for automatic identification of cell populations of known importance (e.g., diagnosis by identification of predefined cell population) and for exploratory analysis of cohorts of flow cytometry assays (e.g., discovery of new correlates of a malignancy). We provide three real-world examples of how unsupervised discovery has been used in basic and clinical research. We also discuss challenges for evaluation of the algorithms developed for (1) identification of cell populations using clustering, (2) identification of specific cell populations, and (3) supervised analysis for discriminating between patient subgroups.

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Authors and Affiliations

  1. Terry Fox Laboratory, BC Cancer Agency, 675 West 10th Avenue, Vancouver BC, V5Z 1L3, Canada

    Nima Aghaeepour & Ryan Brinkman

Authors
  1. Nima Aghaeepour
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  2. Ryan Brinkman
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Correspondence to Ryan Brinkman .

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Editors and Affiliations

  1. Center for Clinical Sciences Research, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, USA

    Harris G. Fienberg

  2. Center for Clinical Sciences Research, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, USA

    Garry P. Nolan

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Aghaeepour, N., Brinkman, R. (2013). Computational Analysis of High-Dimensional Flow Cytometric Data for Diagnosis and Discovery. In: Fienberg, H., Nolan, G. (eds) High-Dimensional Single Cell Analysis. Current Topics in Microbiology and Immunology, vol 377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/82_2013_337

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