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 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 The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression

2020 ◽  
Author(s):  
Florent Petitprez ◽  
Sacha Lévy ◽  
Cheng-Ming Sun ◽  
Maxime Meylan ◽  
Christophe Linhard ◽  
...  
Keyword(s):  
Cell Population ◽  
Stromal Cells ◽  
Stromal Cell ◽  
Relevant Information ◽  
R Package ◽  
Immune Checkpoint Blockade ◽  
Cell Populations ◽  
Tissue Samples ◽  
Flow Cytometry Data ◽  
Transcriptomic Data

AbstractQuantifying tissue-infiltrating immune and stromal cells provides clinically relevant information for various diseases, notably cancer. While numerous methods allow to quantify immune or stromal cells in human tissue samples based on transcriptomic data, very few are available for mouse studies. Here, we introduce murine Microenvironment Cell Population counter (mMCP-counter), a method based on highly specific transcriptomic markers that allow to accurately quantify 12 immune and 4 stromal murine cell populations. We validated mMCP-counter with flow cytometry data. We also showed that mMCP-counter outperforms existing methods. We showed in mouse models of mesothelioma and kidney cancer that mMCP-counter quantification scores are predictive of response to immune checkpoint blockade Finally, we illustrated mMCP-counter’s potential to analyze immune impacts of Alzheimer’s disease. mMCP-counter is available as an R package from GitHub: https://github.com/cit-bioinfo/mMCP-counter.

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 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 optimalFlow: optimal transport approach to flow cytometry gating and population matching

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Eustasio del Barrio ◽  
Hristo Inouzhe ◽  
Jean-Michel Loubes ◽  
Carlos Matrán ◽  
Agustín Mayo-Íscar
Keyword(s):  
Flow Cytometry ◽  
Supervised Learning ◽  
Optimal Transport ◽  
Cell Types ◽  
R Package ◽  
Supervised Machine Learning ◽  
Intrinsic Variability ◽  
Flow Cytometry Data ◽  
Different Characteristics ◽  
Better Than

Abstract Background Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. Results We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow. Conclusions optimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.

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 cytometree: a binary tree algorithm for automatic gating in cytometry analysis

2018 ◽  
Author(s):  
Daniel Commenges ◽  
Chariff Alkhassim ◽  
Raphael Gottardo ◽  
Boris Hejblum ◽  
Rodolphe Thiébaut
Keyword(s):  
Flow Cytometry ◽  
Binary Tree ◽  
Computation Time ◽  
R Package ◽  
Information Criteria ◽  
Supplementary Information ◽  
Flow Cytometry Data ◽  
Human Immunology ◽  
Supplementary Material ◽  
Unsupervised Algorithms

AbstractMotivationFlow cytometry is a powerful technology that allows the high-throughput quantification of dozens of surface and intracellular proteins at the single-cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time-consuming nature.ResultsWe present a new unsupervised algorithm called “cytometree” to perform automated population discovery (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distribution. Node splitting is done according to a normalized difference of Akaike information criteria (AIC) between the two models. Post-processing of the tree structure and derived populations allows us to complete the annotation of the derived populations. The algorithm is shown to perform better than the state-of-the-art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP I) project. The algorithm is also applied to a T-cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open-source available algorithm while requiring the shortest computation time.AvailabilityAn R package named “cytometree” is available on the CRAN [email protected]; [email protected] informationSupplementary data are available.

cyanoFilter: An R package to identify phytoplankton populations from flow cytometry data using cell pigmentation and granularity

2021 ◽  
Vol 460 ◽  
pp. 109743
Author(s):  
Oluwafemi D. Olusoji ◽  
Jurg W. Spaak ◽  
Mark Holmes ◽  
Thomas Neyens ◽  
Marc Aerts ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
R Package ◽  
Flow Cytometry Data
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