ArticleBioinformatics (Oxford, England)2012
Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- A 38-color spectral panel for cost efficient characterization of T cell therapy products.Cytotherapy · 2026Article
- Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.Cytometry. Part B, Clinical cytometry · 2024Article
- Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.Scientific reports · 2024Article
- Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology.Cancers · 2022Review
- PhenoComb: a discovery tool to assess complex phenotypes in high-dimensional single-cell datasets.Bioinformatics advances · 2022Article
- High-Dimensional Immune Monitoring for Chimeric Antigen Receptor T Cell Therapies.Current hematologic malignancy reports · 2021Review
- OMIP-069: Forty-Color Full Spectrum Flow Cytometry Panel for Deep Immunophenotyping of Major Cell Subsets in Human Peripheral Blood.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Review
- 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
- An integrated framework using high-dimensional mass cytometry and fluorescent flow cytometry identifies discrete B cell subsets in patients with red meat allergy.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2019Article
- Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry.Nature neuroscience · 2019Article
- Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia.Nature medicine · 2018Article
- 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
- Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery.Journal of immunology (Baltimore, Md. : 1950) · 2017Article
- Sensitive detection of rare disease-associated cell subsets via representation learning.Nature communications · 2017Article
- flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data.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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9 authors.
Funding
Abstract
motivationPolychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted.
resultsTo address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis). AVAILABILITY: The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: rbrinkman@bccrc.ca.
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