ArticleBMC bioinformatics2021
CytoGLMM: conditional differential analysis for flow and mass cytometry experiments.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A context-aware interpretive framework for lymphocyte immunophenotyping by flow cytometry.Frontiers in immunology · 2026Article
- NKp30 and NKG2D contribute to natural killer cell-mediated recognition of HIV-infected cells.iScience · 2025Article
- Zika virus induces persistent phenotypic changes in natural killer cells distinct from dengue virus infection.bioRxiv : the preprint server for biology · 2025Article
- Mass cytometric analysis of circulating monocyte subsets in a murine model of diabetic gastroparesis.American journal of physiology. Gastrointestinal and liver physiology · 2025Article
- Cord blood innate-like T cell responses in neonates born to healthy women and women living with HIV.Frontiers in immunology · 2025Article
- Distinct immune profiles in children living with HIV based on timing and duration of suppressive antiretroviral treatment.Virology · 2025Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- RUCova: Removal of Unwanted Covariance in mass cytometry data.Bioinformatics (Oxford, England) · 2024Article
- Autophagy counters inflammation-driven glycolytic impairment in aging hematopoietic stem cells.Cell stem cell · 2024Article
- spillR: spillover compensation in mass cytometry data.Bioinformatics (Oxford, England) · 2024Article
- Cytomulate: accurate and efficient simulation of CyTOF data.Genome biology · 2023Article
- High-dimensional profiling of pediatric immune responses to solid organ transplantation.Cell reports. Medicine · 2023Article
- Computational Methods for Single-Cell Proteomics.Annual review of biomedical data science · 2023Review
- Disrupted memory T cell expansion in HIV-exposed uninfected infants is preceded by premature skewing of T cell receptor clonality.bioRxiv : the preprint server for biology · 2023Article
- Advances in HIV Research Using Mass Cytometry.Current HIV/AIDS reports · 2023Review
- Mucosal-homing natural killer cells are associated with aging in persons living with HIV.Cell reports. Medicine · 2022Article
- Challenges in translational machine learning.Human genetics · 2022Review
- Stereotypic Expansion of T Regulatory and Th17 Cells during Infancy Is Disrupted by HIV Exposure and Gut Epithelial Damage.Journal of immunology (Baltimore, Md. : 1950) · 2022Article
- Application of Machine Learning for Cytometry Data.Frontiers in immunology · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundFlow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. Most current data analysis tools compare expressions across many computationally discovered cell types. Our goal is to focus on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees.
resultsDifferential analysis of marker expressions can be difficult due to marker correlations and inter-subject heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: a bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the CytoGLMM R package and workflow for both strategies on a pregnancy dataset.
conclusionOur approach to finding differential proteins in flow and mass cytometry data reduces biases arising from marker correlations and safeguards against false discoveries induced by patient heterogeneity.
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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.