SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 1: Algorithm design

Naim, Iftekhar; Datta, Samir; Rebhahn, Jonathan; Cavenaugh, James S.; Mosmann, Tim R.; Sharma, Gaurav

doi:10.1002/cyto.a.22446

Cited by 94 publications

(108 citation statements)

References 37 publications

(83 reference statements)

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“…Cells in the same sample, or any other sample analyzed under the same conditions, are assigned to clusters based on their probability of belonging to each cluster 10. Therefore, the semantics of populations in similar locations across multiple assigned samples are preserved.…”

Section: Resultsmentioning

confidence: 99%

“…The SWIFT algorithm 10 clusters samples via a three step process with essential details as follows: In the first step, a Gaussian mixture model with specified number of components is fit to the data using the Expectation Maximization (EM) algorithm with an iteratively weighted sampling procedure that improves scalability and resolution of smaller clusters. The second step examines each of these clusters individually, and if necessary, splits individual clusters into additional Gaussian mixtures until all clusters are unimodal along individual dimensions.…”

Section: Methodsmentioning

confidence: 99%

“…The swift_template_combine program (included in the currently‐distributed version of SWIFT 10, 11) combines SCR templates from multiple SWIFT analyses into one Joint template, provided the flow cytometry input parameters are identical across the templates and the per‐channel data transformations are close. Specifically, the Joint template includes all final mixture model components in each of the constituent SCR templates, in the same relative proportions, e.g., if two templates are combined, all proportions will be reduced two‐fold.…”

Section: Methodsmentioning

confidence: 99%

“…In response to the increasing complexity and dimensionality of flow cytometry data, several algorithms have recently been developed to automate the identification and/or quantification of cell populations in flow cytometry data 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. Some algorithms function by automating the traditional manual gating procedure for detecting and isolating specific subpopulations of interest, others attempt to completely resolve cells within a sample into clusters that ideally correspond to biologically meaningful subpopulations.…”

Section: Introductionmentioning

confidence: 99%

“…We have previously described a model‐based algorithm, SWIFT, that provides high‐resolution analysis of high‐dimensional flow cytometry samples and is capable of detecting extremely small subpopulations 8, 10, 11. SWIFT generates a probabilistic mixture model description for the observed data in a sample, and each mixture model component is then equated to a (soft) cluster.…”

Section: Introductionmentioning

confidence: 99%

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Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

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Applications of single‐cell multi‐omics sequencing in deep understanding of brain diseases

Zhang¹,

Lü²,

Shen³

2022

Clinical and Translational Dis

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Single-cell sequencing (SCS) revolutionises our understanding of genomic, transcriptomic, proteomic and epigenomic landscapes of cells within the brain. • Single-cell sequencing technologies provide novel data sources available for exploration of brain development and brain-related diseases. • Applications of single-cell multi-omics sequencing facilitate brain intricacy deciphering as well as diagnosis, targeted therapy and prognosis of a wide range of brain diseases.

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SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 2: Biological evaluation

et al. 2014

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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. © 2014 The Authors. Published by Wiley Periodicals Inc.

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SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 1: Algorithm design

Cited by 94 publications

References 37 publications

Competitive SWIFT cluster templates enhance detection of aging changes

Competitive SWIFT cluster templates enhance detection of aging changes

Applications of single‐cell multi‐omics sequencing in deep understanding of brain diseases

SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 2: Biological evaluation

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