Competitive SWIFT cluster templates enhance detection of aging changes

• Type of Material: Article
• Authors: Jonathan A. Rebhahn, University of Rochester; David R. Roumanes, University of Rochester; Yilin Qi, University of Rochester; Atif Khan, University of Rochester; Juilee Thakar, University of Rochester; Alex Rosenberg, University of Rochester; Frances Lee, Emory University; Sally A. Quataert, University of Rochester; Gaurav Sharma, University of Rochester; Tim R. Mosmann, University of Rochester
• Language: English
• Date: 2016-01-01
• Publisher: Wiley: 12 months
• Publication Version: Final Published Version
• Copyright Statement: © 2016 International Society for Advancement of Cytometry.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/cyto.a.22740
• Title of Journal or Parent Work: Cytometry Part A
• ISSN: 1552-4922
• Volume: 89A
• Issue: 1
• Start Page: 59
• End Page: 70
• Keywords: template; Cell Biology; flow cytometry; DATASETS; Life Sciences & Biomedicine; SWIFT; CYTOMEGALOVIRUS-INFECTION; immunophenotyping; automated analysis; EM algorithm; Science & Technology; AUTOMATED IDENTIFICATION; POPULATIONS; T-CELL DIFFERENTIATION; competitive clustering; FLOW-CYTOMETRY DATA; sample comparison; AGE; Biochemical Research Methods; SUBSETS; ELDERLY HUMANS; Biochemistry & Molecular Biology; CD4(+)
• Research Categories: Health Sciences, General; Health Sciences, Immunology

Abstract

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.