Automated gating of flow cytometry data via robust model‐based clustering

Lo, Kenneth; Brinkman, Ryan R.; Gottardo, Raphaël

doi:10.1002/cyto.a.20531

Cited by 244 publications

(267 citation statements)

References 54 publications

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“…Using the Aging 1 dataset, competitive SWIFT analysis was benchmarked against seven clustering and comparison algorithms. Three of these programs [SamSpectralCluster, 37, FlowMergeCluster, and FlowClustClassify 38], in their GenePattern versions, were not able to cluster the large samples (about 1‐4 million cells, 19 parameters) used in these analyses. ImmPortFLOCK 5 (plus ImmPortCrossSample) could be used in a similar manner as SWIFT, by combining templates from Old and Young subjects.…”

Section: Resultsmentioning

confidence: 99%

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%

Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

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“…179 This robust clustering method is flexible and gives reproducible results. The novel ImageStream technology is combining FC with cell imaging parallel to quantification and visualization of spermatid-specific thioredoxin-3 protein in defective spermatozoa.…”

Section: Future Perspectives For Fc In Spermatologymentioning

confidence: 99%

Flow cytometry for the assessment of animal sperm integrity and functionality: state of the art

Hossain¹,

Johannisson²,

Wallgren³

et al. 2011

Asian J Androl

143

120

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Flow cytometry is now a recognized methodology within animal spermatology, and has moved from being a research tool to become routine in the assessment of animal semen destined to breeding. The availability of 'bench-top' flow cytometers and of newer and versatile markers for cell structure and function had allowed the instrumentation to measure more sperm parameters, from viability to reactiveness when exposed to exogenous stimuli, and to increase our capabilities to sort spermatozoa for potential fertilizing capacity, or chromosomal sex. The present review summarizes the state of the art regarding flow cytometry applied to animal andrology, albeit keeping an open comparative intent. It critically evaluates the present and future capabilities of flow cytometry for the diagnostics of potential fertility and for the development of current reproductive technologies such as sperm freezing, sperm selection and sperm sorting. The flow cytometry methods will probably further revolutionize our understanding of the sperm physiology and their functionality, and will undoubtedly extend its application in isolating many uncharacterized features of spermatozoa. However, continuous follow-up of the methods is a necessity owing to technical developments and the complexity of mapping spermatozoa.

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“…The automatic gating approaches described in (4,8) use mixture models to cluster data. This assigns each location a likelihood measure of belonging to a particular cluster, which is the same process as the fuzzy-clustering Gath-Geva algorithm used in this paper.…”

Section: Original Articlesmentioning

confidence: 99%

“…The APT was specifically designed for situations where objective standards for the definition of cutoffs and response levels are unavailable. Previous work has developed mathematical tools for the objective characterization of flow cytometry data (4)(5)(6)(7)(8), but without providing a framework for their implementation. A recent automated approach used a multivariate neural network approach (9), which is radically different from the techniques used for manual processing.…”

mentioning

confidence: 99%

Analysis of flow cytometry data using an automatic processing tool

et al. 2008

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In spite of recent advances in flow cytometry technology, most cytometry data is still analyzed manually which is labor-intensive for large datasets and prone to bias and inconsistency. We designed an automatic processing tool (APT) to rapidly and consistently define and describe cell populations across large datasets. Image processing, smoothing, and clustering algorithms were used to generate an expert system that automatically reproduces the functionality of commercial manual cytometry processing tools. The algorithms were developed using a dataset collected from CMV-infected infants and combined within a graphical user interface, to create the APT. The APT was used to identify regulatory T-cells in HIV-infected adults, based on expression of FOXP3. Results from the APT were compared directly with the manual analyses of five immunologists and showed close agreement, with a concordance correlation coefficient of 0.96 (95% CI 0.91-0.98). The APT was well accepted by users and able to process around 100 data files per hour. By applying consistent criteria to all data generated by a study, the APT can provide a level of objectivity that is difficult to match using conventional manual analysis. ' 2008 International Society for Advancement of Cytometry Key termsimmunophenotyping; clustering; population; T-cell SINCE the first experiments in identifying cell populations based on the fluorescence characteristics of individual cells were performed in the late 1960s (1), there has been a steady increase in both the scope and availability of this technology to the extent that the benchtop multiparametric flow cytometer is now a standard piece of equipment in an immunology laboratory. The essentials of immunophenotyping have been established for much of that time, as cells are stained with monoclonal antibodies conjugated to flurochromes with nonoverlapping emission peaks, and the intensity of fluorescence in each wavelength is detected (2). Recent years have seen a rapid expansion in the number of parameters that can be assessed on a single cell, and it is now possible to record the intensities of seventeen different flurochromes (3), although most laboratories are still using machines with considerably fewer detectors.While considerable advances have been made in the development of flurochromes and the means to detect them, developments in the tools for analysis of multiparametric data have not kept pace. After measuring fluorescence intensity, most systems output data to an attached computer where each parameter is recorded for each cell. Processing at this stage is based on a limited number of dedicated flow cytometry analysis packages. Nearly all analyses are dependent on the subjective assessment of the operator. An increasing number of studies involve the detection of many different populations and subpopulations in a large number of individuals who may be sampled over a considerable period of time. Under such circumstances, it is unlikely that an individual operator can consistently apply the same criter...

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Automated gating of flow cytometry data via robust model‐based clustering

Cited by 244 publications

References 54 publications

Competitive SWIFT cluster templates enhance detection of aging changes

Competitive SWIFT cluster templates enhance detection of aging changes

Flow cytometry for the assessment of animal sperm integrity and functionality: state of the art

Analysis of flow cytometry data using an automatic processing tool

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