title: SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.
abstract: 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.
authors: David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, University of Rochester, Rochester, New York.
authors: Tim R
authors: TR
authors: Mosmann
authors: Iftekhar
authors: I
authors: Naim
authors: Jonathan
authors: J
authors: Rebhahn
authors: Suprakash
authors: S
authors: Datta
authors: James S
authors: JS
authors: Cavenaugh
authors: Jason M
authors: JM
authors: Weaver
authors: Gaurav
authors: G
authors: Sharma
mesh_terms: D000465:Algorithms; D000941:Antigens / Q000097:blood / Q000276:immunology; D001773:Blood Cells / Q000166:cytology* / Q000276:immunology; D019070:Cell Lineage; D016000:Cluster Analysis; D019295:Computational Biology; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D016011:Normal Distribution; D013601:T-Lymphocytes / Q000166:cytology / Q000276:immunology
chemical_list: D000941:Antigens
keywords: EM algorithm; SWIFT; automated analysis; flow cytometry clustering; ground truth data