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

2018 ◽  
Vol 6 (7) ◽  
pp. e01164 ◽  
Author(s):  
Tyler William Smith ◽  
Paul Kron ◽  
Sara L. Martin
Keyword(s):  
Flow Cytometry ◽  
Genome Size ◽  
R Package ◽  
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 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

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 Genome Size Estimates and Chromosome Numbers of Callicarpa L. (Lamiaceae)

HortScience ◽  
2011 ◽  
Vol 46 (4) ◽  
pp. 567-570 ◽  
Author(s):  
Ryan N. Contreras ◽  
John M. Ruter
Keyword(s):  
Flow Cytometry ◽  
Chromosome Number ◽  
Genome Size ◽  
Chromosome Numbers ◽  
Chromosome Counts ◽  
Ploidy Levels ◽  
Flow Cytometry Data ◽  
Carbol Fuchsin ◽  
Cross Compatibility ◽  
Size Estimates

Genome size estimates and chromosome number information can be useful for studying the evolution or taxonomy of a group and also can be useful for plant breeders in predicting cross-compatibility. Callicarpa L. is a group of ≈140 species with nearly worldwide distribution. There are no estimates of genome size in the literature and the information on chromosome numbers is limited. Genome size estimates based on flow cytometry are reported here for 16 accessions of Callicarpa comprising 14 species in addition to chromosome counts on six species. Chromosome counts were conducted by staining meristematic cells of roots tips using modified carbol fuchsin. Holoploid genome size estimates ranged from 1.34 pg to 3.48 pg with a mean of 1.74 pg. Two tetraploids (2n = 4x = 68; C. salicifolia P'ei & W. Z. Fang and C. macrophylla Vahl GEN09-0081) were identified based on holoploid genome size and confirmed by chromosome counts. There was little variation among species for monoploid genome size. 1Cx-values ranged from 0.67 pg to 0.88 pg with a mean of 0.77 pg. Chromosome counts for six species revealed a base chromosome number of x = 17. Callicarpa chejuensis Y. H. Chung & H. Kim, C. japonica Thunb. ‘Leucocarpa’, C. longissima Merr., and C. rubella Lindl. were confirmed as diploids (2n = 2x = 34). Cytology supported flow cytometry data that C. salicifolia and C. macrophylla GEN09-0081 were tetraploids. The two accessions of C. macrophylla included in the study were found to be of different ploidy levels. The presence of two ploidy levels among and within species indicates that polyploidization events have occurred in the genus.

scholarly journals Endopolyploidy Patterns in Organs of Trifolium Species (Fabaceae)

2014 ◽  
Vol 56 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Valéria Kocová ◽  
Vladislav Kolarcik ◽  
Nikola Straková ◽  
Pavol Mártonfi
Keyword(s):  
Principal Component Analysis ◽  
Flow Cytometry ◽  
Genome Size ◽  
Phylogenetic Relationships ◽  
Natural Populations ◽  
Principal Component ◽  
Component Analysis ◽  
Reproductive Stage ◽  
Interphase Nuclei ◽  
Flow Cytometry Data

Abstract The pattern of endopolyploidy in the genus Trifolium was studied in mature organs of T. montanum and T. repens at reproductive stage, with comparative data for T. pratense, all from natural populations. Endopolyploidy in root, stem, petiole, leaf, inflorescence stalk, sepal, petal, stamen and carpel was detected by flow cytometry. 2C, 4C and 8C nuclei were found in organs of T. montanum and T. repens, and additionally 16C nuclei in organs of T. repens. The organs of T. montanum and T. repens differed in degree of endopolyploidy based on cycle values calculated from flow cytometry data; it was lowest in leaf and sepal in T. montanum and T. repens, and highest in T. montanum in petal and carpel and in T. repens in petiole and inflorescence stalk. These results are also seen in the two or more peaks of interphase nuclei in the flow cytometry histograms. There were significant correlations between the organs of T. pratense and T. repens as well as substantial differences between Trifolium species in the degree of endopolyploidy. T. pratense showed higher absolute endopolyploidy than T. montanum and T. repens. Principal component analysis showed that individuals of T. repens and T. montanum are more similar to each other than to individuals of T. pratense in degree of endopolyploidy. The observed variation between species might be explained by phylogenetic relationships and genome size differences.

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

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

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

2008 ◽  
Vol 73A (4) ◽  
pp. 321-332 ◽  
Author(s):  
Kenneth Lo ◽  
Ryan Remy Brinkman ◽  
Raphael Gottardo
Keyword(s):  
Flow Cytometry ◽  
Model Based Clustering ◽  
Flow Cytometry Data ◽  
Model Based ◽  
Robust Model

First estimates of genome size in ribbon worms (phylum Nemertea) using flow cytometry and Feulgen image analysis densitometry

2014 ◽  
Vol 92 (10) ◽  
pp. 847-851 ◽  
Author(s):  
Kelly L. Mulligan ◽  
Terra C. Hiebert ◽  
Nicholas W. Jeffery ◽  
T. Ryan Gregory
Keyword(s):  
Flow Cytometry ◽  
Image Analysis ◽  
Body Size ◽  
Genome Size ◽  
Positive Relationship ◽  
Nuclear Dna ◽  
Nuclear Dna Content ◽  
Size Estimation ◽  
Size Diversity ◽  
Size Estimates

Ribbon worms (phylum Nemertea) are among several animal groups that have been overlooked in past studies of genome-size diversity. Here, we report genome-size estimates for eight species of nemerteans, including representatives of the major lineages in the phylum. Genome sizes in these species ranged more than fivefold, and there was some indication of a positive relationship with body size. Somatic endopolyploidy also appears to be common in these animals. Importantly, this study demonstrates that both of the most common methods of genome-size estimation (flow cytometry and Feulgen image analysis densitometry) can be used to assess genome size in ribbon worms, thereby facilitating additional efforts to investigate patterns of variability in nuclear DNA content in this phylum.

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