ArticleBMC bioinformatics2012
Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Computationally efficient multi-sample flow cytometry data analysis using Gaussian mixture models.BMC bioinformatics · 2025Article
- Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology.Cancers · 2022Review
- optimalFlow: optimal transport approach to flow cytometry gating and population matching.BMC bioinformatics · 2020Article
- flowVS: channel-specific variance stabilization in flow cytometry.BMC bioinformatics · 2016Article
- Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples.Frontiers in oncology · 2016Article
- Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- Earth Mover's Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations.PloS one · 2016Article
- A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects.BMC bioinformatics · 2014Article
- Critical assessment of automated flow cytometry data analysis techniques.Nature methods · 2013Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWhen flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past to study such changes, but high-dimensional experiments necessitate developing new computational approaches to this problem. A robust solution to this problem is to construct distinct templates to summarize all samples from a class, and then to compare these templates to study the changes across classes or conditions.
resultsWe designed a hierarchical algorithm, flowMatch, to first match the corresponding clusters across samples for producing robust meta-clusters, and to then construct a high-dimensional template as a collection of meta-clusters for each class of samples. We applied the algorithm on flow cytometry data obtained from human blood cells before and after stimulation with anti-CD3 monoclonal antibody, which is reported to change phosphorylation responses of memory and naive T cells. The flowMatch algorithm is able to construct representative templates from the samples before and after stimulation, and to match corresponding meta-clusters across templates. The templates of the pre-stimulation and post-stimulation data corresponding to memory and naive T cell populations clearly show, at the level of the meta-clusters, the overall phosphorylation shift due to the stimulation.
conclusionsWe concisely represent each class of samples by a template consisting of a collection of meta-clusters (representative abstract populations). Using flowMatch, the meta-clusters across samples can be matched to assess overall differences among the samples of various phenotypes or time-points.
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