ReviewNature biotechnology2016
Revealing the vectors of cellular identity with single-cell genomics.
Review in Nature biotechnology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05209061 (Cellular Composition of the Human Appendix Using Single-cell RNA and Single-cell ATAC-sequencing.), which is not on this map. Cited by 366 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.
Cellular Composition of the Human Appendix Using Single-cell RNA and Single-cell ATAC-sequencing.
Who cites it
366 citing papers in PubMed.
- Donor-aware integrative single-cell analysis distinguishes consistent memory B-cell programmes from donor-sensitive state boundaries in untreated rheumatoid arthritis.Functional & integrative genomics · 2026Article
- FlowMap: Geometry-Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories.bioRxiv : the preprint server for biology · 2026Article
- Article
- Unicellular and Multicellular Modes of Selection Impose Distinct Constraints on Cellular Phenotype Evolution.bioRxiv : the preprint server for biology · 2026Article
- Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.Briefings in bioinformatics · 2026Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Transcriptional landscape of direct reprogramming toward the hematopoietic lineage.bioRxiv : the preprint server for biology · 2026Article
- Statescope: an integrative deconvolution framework for discovering cell states in tumors.Nature communications · 2026Article
- Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- The cell cloud: Adopting systems biology concepts in the era of single-cell immunology.PLoS biology · 2026Article
- Integrated microfluidic biosensors: shaping the future of quantitative life sciences and on-chip molecular diagnostics.Lab on a chip · 2026Review
- ScEnsemble: weighted hypergraph ensemble clustering for single-cell RNA sequencing.BMC bioinformatics · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- Single-cell RNA sequencing of the post-spinal cord injury dorsal root ganglia in cynomolgus monkeys: Elucidation of the cellular immune microenvironment of the central nervous system.Neural regeneration research · 2026Article
- Shortcomings of silhouette in single-cell integration benchmarking.Nature biotechnology · 2026Article
- Effects of supplementing concentrates with different energy levels on growth performance, blood indicators, and fecal microbiota of Mongolian horses in late gestation.BMC veterinary research · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- DestinyNet: A deep-learning framework for cell-fate analysis from lineage-tracing single-cell RNA sequencing data.Patterns (New York, N.Y.) · 2026Article
- Integrative analysis of single-cell and bulk RNA sequencing data using machine learning identifies PTGDS as a key subsynovial fibroblast gene in osteoarthritis.Journal of molecular histology · 2026Article
- Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
306 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.
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.