scholarly journals Model-based cell clustering and population tracking for time-series flow cytometry data

2019 ◽  
Author(s):  
Kodai Minoura ◽  
Ko Abe ◽  
Yuka Maeda ◽  
Hiroyoshi Nishikawa ◽  
Teppei Shimamura
Keyword(s):  
Flow Cytometry ◽  
Time Series ◽  
Population Dynamics ◽  
Cell Population ◽  
Mixture Distribution ◽  
Gaussian Mixture ◽  
Time Dependent ◽  
Cell Populations ◽  
Flow Cytometry Data ◽  
Multivariate Gaussian

AbstractMotivationModern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis.Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data.ResultsWe propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior.ConclusionsOur results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

scholarly journals Model-based cell clustering and population tracking for time-series flow cytometry data

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Kodai Minoura ◽  
Ko Abe ◽  
Yuka Maeda ◽  
Hiroyoshi Nishikawa ◽  
Teppei Shimamura
Keyword(s):  
Flow Cytometry ◽  
Time Series ◽  
Population Dynamics ◽  
Cell Population ◽  
Mixture Distribution ◽  
Gaussian Mixture ◽  
Time Dependent ◽  
Cell Populations ◽  
Flow Cytometry Data ◽  
Multivariate Gaussian

Abstract Background Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis. Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data. Results We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Conclusions Our results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

scholarly journals Automated identification of maximal differential cell populations in flow cytometry data

2019 ◽  
Author(s):  
Alice Yue ◽  
Cedric Chauve ◽  
Maxwell Libbrecht ◽  
Ryan R. Brinkman
Keyword(s):  
Flow Cytometry ◽  
Cell Population ◽  
R Package ◽  
Cell Populations ◽  
Visualization Tool ◽  
Automated Identification ◽  
Flow Cytometry Data ◽  
New Class ◽  
Differential Cell ◽  
Related Population

AbstractWe introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g. disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice-based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and will be available BioConductor.

scholarly journals Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure

2015 ◽  
Vol 89 (1) ◽  
pp. 71-88 ◽  
Author(s):  
Chiaowen Hsiao ◽  
Mengya Liu ◽  
Rick Stanton ◽  
Monnie McGee ◽  
Yu Qian ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Distance Measure ◽  
Cell Populations ◽  
Test Statistic ◽  
Flow Cytometry Data

scholarly journals An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics

2011 ◽  
Vol 9 (1) ◽  
pp. 89-112 ◽  
Author(s):  
Daniel A. Charlebois ◽  
Jukka Intosalmi ◽  
Dawn Fraser ◽  
Mads Kærn
Keyword(s):  
Gene Expression ◽  
Population Dynamics ◽  
Steady State ◽  
Stochastic Simulation ◽  
Molecular Level ◽  
Single Cells ◽  
Heterogeneous Population ◽  
Population Heterogeneity ◽  
Time Dependent ◽  
Cell Populations

AbstractWe present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell populations. To benchmark performance, we compare simulation results with steady-state and time-dependent analytical solutions for several scenarios, including steady-state and time-dependent gene expression, and the effects on population heterogeneity of cell growth, division, and DNA replication. This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression. We also use the algorithm to model gene expression dynamics within “bet-hedging” cell populations during their adaption to environmental stress. These simulations indicate that the algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics, relevant physiological details and phenotypic variability.

scholarly journals flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification

2014 ◽  
Vol 31 (4) ◽  
pp. 606-607 ◽  
Author(s):  
Mehrnoush Malek ◽  
Mohammad Jafar Taghiyar ◽  
Lauren Chong ◽  
Greg Finak ◽  
Raphael Gottardo ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Cell Population ◽  
Flow Cytometry Data

scholarly journals Rapid cell population identification in flow cytometry data

2010 ◽  
Vol 79A (1) ◽  
pp. 6-13 ◽  
Author(s):  
Nima Aghaeepour ◽  
Radina Nikolic ◽  
Holger H. Hoos ◽  
Ryan R. Brinkman
Keyword(s):  
Flow Cytometry ◽  
Cell Population ◽  
Flow Cytometry Data

scholarly journals PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure

mSphere ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Peter Rubbens ◽  
Ruben Props ◽  
Frederiek-Maarten Kerckhof ◽  
Nico Boon ◽  
Willem Waegeman
Keyword(s):  
Flow Cytometry ◽  
Community Structure ◽  
Microbial Community ◽  
Microbial Community Structure ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Community Diversity ◽  
Data Sets ◽  
Natural Ecosystems ◽  
Flow Cytometry Data

ABSTRACT Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics. IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

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