ArticleF1000Research2017
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets.
Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 350 papers.
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Who cites it
350 citing papers in PubMed.
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- Mass Cytometry Reveals Cell Type-specific Immune Remodeling in Placentas of Women with Chronic Hepatitis B Infection.Journal of clinical and translational hepatology · 2026Article
- ConvexGating infers gating strategies from clusters in single cell cytometry data.Nature communications · 2026Article
- Circulating B cell and T cell activation states predict clinical outcomes in melanoma and reveal dynamic immune reinvigoration with checkpoint inhibitor immunotherapy.Journal for immunotherapy of cancer · 2026Article
- Pyoderma gangrenosum caused by the molecular uncoupling of OTULIN catalytic activity and LUBAC binding.Nature immunology · 2026Article
- Protocol for single-cell epigenetic profiling in human organoids and tumoroids with Epi-CyTOF.STAR protocols · 2026Article
- Bile acids mediate liver-bone marrow crosstalk.iScience · 2026Article
290 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signalling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals).
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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.