ArticlePatterns (New York, N.Y.)2021
New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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
27 citing papers in PubMed.
- Variable selection methods for predicting clinical outcomes following allogeneic hematopoietic cell transplantation.Scientific reports · 2021Trial
- Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study.PLoS medicine · 2026Article
- Application and characterization of the multiple instance learning framework in flow cytometry.Scientific reports · 2026Article
- Tumour-associated vasculature in T cell homing and immunity: opportunities for cancer therapy.Nature reviews. Immunology · 2025Review
- Phenotyping of human platelets in response to platelet agonists and inhibitors using multiparameter flow cytometry and unbiased high-dimensional analysis.Research and practice in thrombosis and haemostasis · 2025Article
- A minimum valency of 4 is required for robust activation of platelets in flow cytometry by multivalent nanobodies to Glycoprotein VI, C-type lectin-like receptor 2 and Platelet Endothelial Aggregation Receptor 1.Research and practice in thrombosis and haemostasis · 2025Article
- Insulin resistance in type 1 diabetes is a key modulator of platelet hyperreactivity.Diabetologia · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- A General Framework for Comparing Embedding Visualizations Across Class-Label Hierarchies.IEEE transactions on visualization and computer graphics · 2025Article
- OMIP-102: 50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2024Article
- 50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells.bioRxiv : the preprint server for biology · 2023Article
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- Flow Cytometry: The Next Revolution.Cells · 2023Review
- Phenotypic and functional heterogeneity of monocytes in health and cancer in the era of high dimensional technologies.Blood reviews · 2023Review
- T-Cell Mediated Immunity in Merkel Cell Carcinoma.Cancers · 2022Review
- A path forward to improving the specificity of immunotherapies.Clinical and translational medicine · 2022Article
- Deciphering immunoregulatory vulnerabilities in human cancers.Nature immunology · 2022Article
- Redistribution of CD8+ T cell subsets in metastatic renal cell carcinoma patients treated with anti-PD-1 therapy.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
- Identifying Candidates for Immunotherapy among Patients with Non-Melanoma Skin Cancer: A Review of the Potential Predictors of Response.Journal of clinical medicine · 2022Review
- Article
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Authors and funding
12 authors.
Funding
Abstract
We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength. We apply FAUST to data from a Merkel cell carcinoma anti-PD-1 trial and discover pre-treatment effector memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. Using FAUST, we then validate these correlates in cryopreserved peripheral blood mononuclear cell samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Finally, we show how FAUST's phenotypes can be used to perform cross-study data integration in the presence of diverse staining panels. Together, these results establish FAUST as a powerful new approach for unbiased discovery in single-cell cytometry.
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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.