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

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.

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

10.1101/335554 ◽

2018 ◽

Cited By ~ 1

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.

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Automated identification of maximal differential cell populations in flow cytometry data

10.1101/837765 ◽

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.

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flowPloidy: An R package for genome size and ploidy assessment of flow cytometry data

Applications in Plant Sciences ◽

10.1002/aps3.1164 ◽

2018 ◽

Vol 6 (7) ◽

pp. e01164 ◽

Cited By ~ 6

Author(s):

Tyler William Smith ◽

Paul Kron ◽

Sara L. Martin

Keyword(s):

Flow Cytometry ◽

Genome Size ◽

R Package ◽

Flow Cytometry Data

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Recent Bioinformatics Advances in the Analysis of High Throughput Flow Cytometry Data

Advances in Bioinformatics ◽

10.1155/2009/461763 ◽

2009 ◽

Vol 2009 ◽

pp. 1-2

Author(s):

Raphael Gottardo ◽

Ryan R. Brinkman ◽

George Luta ◽

Matt P. Wand

Keyword(s):

Flow Cytometry ◽

High Throughput ◽

Flow Cytometry Data

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

Cytometry Part A ◽

10.1002/cyto.a.20531 ◽

2008 ◽

Vol 73A (4) ◽

pp. 321-332 ◽

Cited By ~ 159

Author(s):

Kenneth Lo ◽

Ryan Remy Brinkman ◽

Raphael Gottardo

Keyword(s):

Flow Cytometry ◽

Model Based Clustering ◽

Flow Cytometry Data ◽

Model Based ◽

Robust Model

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A Multidimensional Flow Cytometry Data Classification

2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering ◽

10.1109/bibe.2009.20 ◽

2009 ◽

Cited By ~ 3

Author(s):

Ming-Chih Shih ◽

Shou-Hsuan Stephen Huang ◽

Chung-Che Jeff Chang

Keyword(s):

Flow Cytometry ◽

Data Classification ◽

Flow Cytometry Data

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Natural polyploidy in Vanilla planifolia (Orchidaceae)

Genome ◽

10.1139/g08-068 ◽

2008 ◽

Vol 51 (10) ◽

pp. 816-826 ◽

Cited By ~ 29

Author(s):

Séverine Bory ◽

Olivier Catrice ◽

Spencer Brown ◽

Ilia J. Leitch ◽

Rodolphe Gigant ◽

...

Keyword(s):

Flow Cytometry ◽

Phenotypic Variation ◽

Chromosome Numbers ◽

Reunion Island ◽

Vanilla Planifolia ◽

Root Cells ◽

Flow Cytometry Data ◽

Important Variation ◽

Réunion Island ◽

Stomatal Length

Vanilla planifolia accessions cultivated in Reunion Island display important phenotypic variation, but little genetic diversity is demonstrated by AFLP and SSR markers. This study, based on analyses of flow cytometry data, Feulgen microdensitometry data, chromosome counts, and stomatal length measurements, was performed to determine whether polyploidy could be responsible for some of the intraspecific phenotypic variation observed. Vanilla planifolia exhibited an important variation in somatic chromosome number in root cells, as well as endoreplication as revealed by flow cytometry. Nevertheless, the 2C-values of the 50 accessions studied segregated into three distinct groups averaging 5.03 pg (for most accessions), 7.67 pg (for the ‘Stérile’ phenotypes), and 10.00 pg (for the ‘Grosse Vanille’ phenotypes). For the three groups, chromosome numbers varied from 16 to 32, 16 to 38, and 22 to 54 chromosomes per cell, respectively. The stomatal length showed a significant variation from 37.75 µm to 48.25 µm. Given that 2C-values, mean chromosome numbers, and stomatal lengths were positively correlated and that ‘Stérile’ and ‘Grosse Vanille’ accessions were indistinguishable from ‘Classique’ accessions using molecular markers, the occurrence of recent autotriploid and autotetraploid types in Reunion Island is supported. This is the first report showing evidence of a recent autopolyploidy in V. planifolia contributing to the phenotypic variation observed in this species.

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Normative data for flow cytometry immunophenotyping of benign lymph nodes sampled by surgical biopsy

Journal of Clinical Pathology ◽

10.1136/jclinpath-2017-204687 ◽

2017 ◽

Vol 71 (2) ◽

pp. 174-179 ◽

Cited By ~ 8

Author(s):

Gregory David Scott ◽

Susan K Atwater ◽

Dita A Gratzinger

Keyword(s):

Flow Cytometry ◽

Lymph Node ◽

T Cell ◽

Lymph Nodes ◽

Inflammatory Disease ◽

Clinical History ◽

Surgical Biopsy ◽

Data Set ◽

Flow Cytometry Data ◽

Benign Lymph Node

AimsTo create clinically relevant normative flow cytometry data for understudied benign lymph nodes and characterise outliers.MethodsClinical, histological and flow cytometry data were collected and distributions summarised for 380 benign lymph node excisional biopsies. Outliers for kappa:lambda light chain ratio, CD10:CD19 coexpression, CD5:CD19 coexpression, CD4:CD8 ratios and CD7 loss were summarised for histological pattern, concomitant diseases and follow-up course.ResultsWe generated the largest data set of benign lymph node immunophenotypes by an order of magnitude. B and T cell antigen outliers often had background immunosuppression or inflammatory disease but did not subsequently develop lymphoma.ConclusionsDiagnostic immunophenotyping data from benign lymph nodes provide normative ranges for clinical use. Outliers raising suspicion for B or T cell lymphoma are not infrequent (26% of benign lymph nodes). Caution is indicated when interpreting outliers in the absence of excisional biopsy or clinical history, particularly in patients with concomitant immunosuppression or inflammatory disease.

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