ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2018
Modeling of cytometry data in logarithmic space: When is a bimodal distribution not bimodal?
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Review of flow cytometry as a tool for cell and gene therapy.Cytotherapy · 2024Review
- Article
- Quantifying the Dynamics of Hematopoiesis by In Vivo IdU Pulse-Chase, Mass Cytometry, and Mathematical Modeling.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Critical slowing down in biochemical networks with feedback.Physical review. E · 2019Article
- Computational Structural Biology: Successes, Future Directions, and Challenges.Molecules (Basel, Switzerland) · 2019Review
- Universality of biochemical feedback and its application to immune cells.Physical review. E · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Recent efforts in systems immunology lead researchers to build quantitative models of cell activation and differentiation. One goal is to account for the distributions of proteins from single-cell measurements by flow cytometry or mass cytometry as readout of biological regulation. In that context, large cell-to-cell variability is often observed in biological quantities. We show here that these readouts, viewed in logarithmic scale may result in two easily-distinguishable modes, while the underlying distribution (in linear scale) is unimodal. We introduce a simple mathematical test to highlight this mismatch. We then dissect the flow of influence of cell-to-cell variability proposing a graphical model which motivates higher-dimensional analysis of the data. Finally we show how acquiring additional biological information can be used to reduce uncertainty introduced by cell-to-cell variability, helping to clarify whether the data is uni- or bimodal. This communication has cautionary implications for manual and automatic gating strategies, as well as clustering and modeling of single-cell measurements. © 2018 International Society for Advancement of Cytometry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.