Journal
CYTOMETRY PART A
Volume 85, Issue 5, Pages 422-433Publisher
WILEY
DOI: 10.1002/cyto.a.22445
Keywords
SWIFT; EM algorithm; flow cytometry clustering; ground truth data; automated analysis
Categories
Funding
- National Institute for Allergy and Infectious Diseases through the Rochester Human Immunology Center [R24AI054953]
- National Institute for Allergy and Infectious Diseases New York Influenza Center of Excellence [HHSN266200700008C]
- National Center for Advancing Translational Sciences through the University of Rochester CTSA [UL1 RR024160]
- National Center for Research Resources
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A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT. (c) 2014 The Authors. Published by Wiley Periodicals Inc.
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