ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2012
RchyOptimyx: cellular hierarchy optimization for flow cytometry.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study.PLoS medicine · 2026Article
- Deciphering cellular complexity: advances and future directions in single-cell protein analysis.Frontiers in bioengineering and biotechnology · 2024Review
- Flow Cytometry: The Next Revolution.Cells · 2023Review
- Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology.Cancers · 2022Review
- High-Dimensional Immune Monitoring for Chimeric Antigen Receptor T Cell Therapies.Current hematologic malignancy reports · 2021Review
- Review
- Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry.Computational and structural biotechnology journal · 2020Review
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Data-Driven Flow Cytometry Analysis.Methods in molecular biology (Clifton, N.J.) · 2019Review
- Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry.Nature neuroscience · 2019Article
- Scalable Conjugation and Characterization of Immunoglobulins with Stable Mass Isotope Reporters for Single-Cell Mass Cytometry Analysis.Methods in molecular biology (Clifton, N.J.) · 2019Article
- High throughput automated analysis of big flow cytometry data.Methods (San Diego, Calif.) · 2018Article
- Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies.European journal of immunology · 2017Article
- Comparative exploration of multidimensional flow cytometry software: a model approach evaluating T cell polyfunctional behavior.Journal of leukocyte biology · 2017Article
- Biologically Relevant Heterogeneity: Metrics and Practical Insights.SLAS discovery : advancing life sciences R & D · 2017Review
- Visualization and cellular hierarchy inference of single-cell data using SPADE.Nature protocols · 2016Article
- Competitive SWIFT cluster templates enhance detection of aging changes.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
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7 authors.
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Abstract
Analysis of high-dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets. We present three proof-of-concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non-redundant marker set to identify a target cell population.
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