ArticleBMC bioinformatics2017
Cell type discovery and representation in the era of high-content single cell phenotyping.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases.bioRxiv : the preprint server for biology · 2026Article
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- Article
- Gene panel selection for targeted spatial transcriptomics.Genome biology · 2024Article
- Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.BMC methods · 2024Article
- Review
- Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing.The Biochemical journal · 2023Review
- Gene panel selection for targeted spatial transcriptomics.bioRxiv : the preprint server for biology · 2023Article
- Brain Data Standards - A method for building data-driven cell-type ontologies.Scientific data · 2023Article
- Metabolomics studies of cell-cell interactions using single cell mass spectrometry combined with fluorescence microscopy.Chemical science · 2022Article
- Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.Briefings in bioinformatics · 2022Article
- Transcriptomic Crosstalk between Gliomas and Telencephalic Neural Stem and Progenitor Cells for Defining Heterogeneity and Targeted Signaling Pathways.International journal of molecular sciences · 2021Review
- A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.Genome research · 2021Article
- Article
- FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test.Briefings in bioinformatics · 2021Article
- Bridging the Brain and Data Sciences.Big data · 2021Article
- Predicting candidate genes from phenotypes, functions and anatomical site of expression.Bioinformatics (Oxford, England) · 2021Article
- Machine Learning-Based Single Cell and Integrative Analysis Reveals That Baseline mDC Predisposition Correlates With Hepatitis B Vaccine Antibody Response.Frontiers in immunology · 2021Article
- A community-based transcriptomics classification and nomenclature of neocortical cell types.Nature neuroscience · 2020Review
- CReSCENT: CanceR Single Cell ExpressioN Toolkit.Nucleic acids research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundA fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology.
resultsIn this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations.
conclusionThe advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.
Indexed as
Identifiers
What OpenQuestion holds
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.