Competitive SWIFT cluster templates enhance detection of aging changes

• Published in: Cytometry. Part A Vol. 89; no. 1; pp. 59 - 70
• Main Authors: Rebhahn, Jonathan A., Roumanes, David R., Qi, Yilin, Khan, Atif, Thakar, Juilee, Rosenberg, Alex, Lee, F. Eun‐Hyung, Quataert, Sally A., Sharma, Gaurav, Mosmann, Tim R.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2016; John Wiley and Sons Inc
• Subjects: Adult; Aged; Aged, 80 and over; Aging; Algorithms; automated analysis; Biomarkers - analysis; Change detection; Cluster Analysis; Clustering; Clusters; Competition; competitive clustering; Computational Analysis of Flow Cytometry Data (PART II); Computational Biology - methods; Cytometry; Data Interpretation, Statistical; Datasets; EM algorithm; Female; Flow cytometry; Flow Cytometry - methods; Fluorescence; Geriatrics; Humans; immunophenotyping; Immunophenotyping - methods; Leukocytes, Mononuclear - cytology; Leukocytes, Mononuclear - immunology; Male; Middle Aged; Older people; sample comparison; Sex Factors; Sharpening; Subpopulations; SWIFT; template; Young Adult; template; immunophenotyping; competitive clustering; SWIFT; sample comparison; flow cytometry; automated analysis; EM algorithm
• DOI: 10.1002/cyto.a.22740
• ISSN: 1552-4922

Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets. © 2015 The Authors. Published Wiley Periodicals Inc.