scholarly journals Automatic Clustering of Flow Cytometry Data with Density-Based Merging

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
Guenther Walther ◽  
Noah Zimmerman ◽  
Wayne Moore ◽  
David Parks ◽  
Stephen Meehan ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Parametric Model ◽  
Two Dimensional ◽  
Laboratory Setting ◽  
Flow Cytometry Data ◽  
Current Limit ◽  
Automatic Clustering ◽  
Automated Tools ◽  
A Current ◽  
Number Of Cells

The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

scholarly journals Multi-set Pre-processing of Multicolor Flow Cytometry Data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rita Folcarelli ◽  
Gerjen H. Tinnevelt ◽  
Bart Hilvering ◽  
Kristiaan Wouters ◽  
Selma van Staveren ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cells ◽  
Classification Performance ◽  
Multivariate Methods ◽  
Measurement Variability ◽  
Flow Cytometry Data ◽  
Cell Sample ◽  
The Difference ◽  
Analytical Technology ◽  
Number Of Cells

Abstract Flow Cytometry is an analytical technology to simultaneously measure multiple markers per single cell. Ten thousands to millions of single cells can be measured per sample and each sample may contain a different number of cells. All samples may be bundled together, leading to a ‘multi-set’ structure. Many multivariate methods have been developed for Flow Cytometry data but none of them considers this structure in their quantitative handling of the data. The standard pre-processing used by existing multivariate methods provides models mainly influenced by the samples with more cells, while such a model should provide a balanced view of the biomedical information within all measurements. We propose an alternative ‘multi-set’ preprocessing that corrects for the difference in number of cells measured, balancing the relative importance of each multi-cell sample in the data while using all data collected from these expensive analyses. Moreover, one case example shows how multi-set pre-processing may benefit removal of undesired measurement-to-measurement variability and another where class-based multi-set pre-processing enhances the studied response upon comparison to the control reference samples. Our results show that adjusting data analysis algorithms to consider this multi-set structure may greatly benefit immunological insight and classification performance of Flow Cytometry data.

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

Natural polyploidy in Vanilla planifolia (Orchidaceae)

Genome ◽  
2008 ◽  
Vol 51 (10) ◽  
pp. 816-826 ◽  
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.

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

2017 ◽  
Vol 71 (2) ◽  
pp. 174-179 ◽  
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.

Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

2018 ◽  
Vol 145 ◽  
pp. 73-82 ◽  
Author(s):  
Ruben Props ◽  
Peter Rubbens ◽  
Michael Besmer ◽  
Benjamin Buysschaert ◽  
Jurg Sigrist ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Drinking Water ◽  
Microbial Community ◽  
Flow Cytometry Data ◽  
Advanced Analysis
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