ReviewCancer informatics2014
Bayesian hierarchical models for protein networks in single-cell mass cytometry.
Review in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
3 citing papers in PubMed.
- Cytomulate: accurate and efficient simulation of CyTOF data.Genome biology · 2023Article
- Proteomics, Personalized Medicine and Cancer.Cancers · 2021Review
- Systems biology of neurodegenerative diseases.Integrative biology : quantitative biosciences from nano to macro · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We propose a class of hierarchical models to investigate the protein functional network of cellular markers. We consider a novel data set from single-cell proteomics. The data are generated from single-cell mass cytometry experiments, in which protein expression is measured within an individual cell for multiple markers. Tens of thousands of cells are measured serving as biological replicates. Applying the Bayesian models, we report protein functional networks under different experimental conditions and the differences between the networks, ie, differential networks. We also present the differential network in a novel fashion that allows direct observation of the links between the experimental agent and its putative targeted proteins based on posterior inference. Our method serves as a powerful tool for studying molecular interactions at cellular level.
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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.