Universal cell type identifier based on number theory

Cosma, Antonio

doi:10.1002/cyto.a.23346

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…High parameter cytometry (>20) allows for benchmarking alternative definitions of cell subsets together with cell classification and phenotype reporting tools (27,86,87). These approaches have the potential to reduce the current cell subset nomenclature issues.…”

Section: How Would Standardization Develop In the Near Future?mentioning

confidence: 99%

Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Kalina

2019

Cytometry Pt A

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There is an agreement in the field that interlaboratory reproducibility of flow cytometry measurements as well as the whole studies might be improved by a consensual use of methodological approach. Typically, a consensus is made on a crucial markers needed in the immunostaining panel, sometimes on the particular fluorochrome conjugates and rarely on a complete set of methods for sample preparation. The term "standardization" is used to describe the complete set of methodical steps, while "harmonization" is used for partial agreement on the method. Standardization can provide a platform for improved reproducibility of cytometry results over prolonged periods of time, across different sites and across different instruments. For the purpose of structured discussion, several desired aims are described: common interpretation of the immunophenotype definition of a target subset, accurate quantification, reproducible pattern of a multicolor immunophenotype, and reproducible intensity of all measured parameters. An overview of how standardization was approached by several large consortia is provided: EuroFlow, The ONE Study, Human Immunology Project Consortium (HIPC), and several other groups. Their particular aims and the tools adopted to reach those aims are noted. How those standardization efforts were adopted in the field and how the resulting outcome was evaluated is reviewed. Multiple challenges in the instrument hardware design, instrument setup tools, reagent design, and quality features need to be addressed to achieve optimal standardization. Furthermore, the aims of different studies vary, and thus, the reasonable requirements for standardization differ. A framework of reference for the reasonable outcomes of different approaches is offered. Finally, it is argued that complete standardization is important not only for the reproducibility of measurements but also for education, for quality assessment and for algorithmic data analysis. The different standardized approaches can and in fact should serve as benchmarking reference tools for the development of future flow cytometry studies.

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Section: How Would Standardization Develop In the Near Future?mentioning

confidence: 99%

Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Kalina

2019

Cytometry Pt A

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“…In that sense, MegaClust, as well as any other clustering algorithms, are unbiased, meaning not biased by human subjective visual perception, since they rely on mathematical computations (i.e. distance metric) and can potentially detect undescribed biological populations (e.g., populations in state transition or rare populations) (30). However, the parameter responsible directly or indirectly for the number of clusters (nmin in the case of MegsClust) will have to be fine-tuned, so that the number of clusters reported exceeds the number of biological populations described in the literature.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery

et al. 2021

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Data obtained with cytometry are increasingly complex and their interrogation impacts the type and quality of knowledge gained. Conventional supervised analyses are limited to pre-defined cell populations and do not exploit the full potential of data. Here, in the context of a clinical trial of cancer patients treated with radiotherapy, we performed longitudinal flow cytometry analyses to identify multiple distinct cell populations in circulating whole blood. We cross-compared the results from state-of-the-art recommended supervised analyses with results from MegaClust, a high-performance data-driven clustering algorithm allowing fast and robust identification of cell-type populations. Ten distinct cell populations were accurately identified by supervised analyses, including main T, B, dendritic cell (DC), natural killer (NK) and monocytes subsets. While all ten subsets were also identified with MegaClust, additional cell populations were revealed (e.g. CD4+HLA-DR+ and NKT-like subsets), and DC profiling was enriched by the assignment of additional subset-specific markers. Comparison between transcriptomic profiles of purified DC populations and publicly available datasets confirmed the accuracy of the unsupervised clustering algorithm and demonstrated its potential to identify rare and scarcely described cell subsets. Our observations show that data-driven analyses of cytometry data significantly enrich the amount and quality of knowledge gained, representing an important step in refining the characterization of immune responses.

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“…1). The rst step consists of associating prime numbers to the UniProt list to have a functional PPS as previously described (2). The list of prime numbers and the associated list of proteins is available as supplementary material at the following link ( https://public.tableau.com/app/pro le/fanny2212/viz/PrimeNumbers-UniProt/PrimeNumbers-UniProtassociation ).…”

Section: Methodsmentioning

confidence: 99%

“…Thus far, there is no standard way to represent a gating strategy and exchange this information among software, publications, and repositories. I previously proposed a standard to name markers and cell types based on prime numbers and the fundamental theorem of arithmetic (2). An ordered list of markers is associated with the list of prime numbers according to a one-to-one relationship.…”

Section: Introductionmentioning

confidence: 99%

Unique Gating Strategy Identifier based on the Prime Population System and Gödel Numbers

Cosma

2024

Preprint

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Gating is a fundamental and essential process in cytometry data analysis since it defines cell types of interest. Currently, there is no universally accepted method for representing and sharing gating strategies among software, publications, and repositories. I propose using the Prime Population system combined with a modified version of Gödel Numbers to identify any gating strategy uniquely. The Prime Population System is first used to identify gates on bivariate plots; successively, Gödel Numbers are used to set the sequence of the hierarchical gating strategy. The process results are unique identifiers for any existing and future gating strategy. This method represents a further step towards the arithmetization of cytometry metadata.

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Universal cell type identifier based on number theory

Cited by 3 publications

References 9 publications

Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges

Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery

Unique Gating Strategy Identifier based on the Prime Population System and Gödel Numbers

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