ReviewNature reviews. Genetics2022
Temporal modelling using single-cell transcriptomics.
Review in Nature reviews. Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 1 of them a synthesis that pooled it.
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
137 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural stem cell transplantation in rodent models of traumatic brain injury: a systematic review and meta-analysis.Frontiers in bioengineering and biotechnology · 2026Pooled it
- From blueprint to build: Metal ions in peripheral nerve development and engineering regeneration.Bioactive materials · 2026Review
- Genome-wide chromatin recording resolves dynamic cell state changes.Cell systems · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Dynamic prediction of HIV-related incomplete immune reconstitution: A multicenter, large cohort study using advanced joint modeling.Science advances · 2026Article
- scTimeBench: a streamlined benchmarking platform for single-cell time-series analysis.Bioinformatics (Oxford, England) · 2026Article
- RNA Sequencing Technologies in Acute Lymphoblastic Leukemia: A Comparative Technical Review.Current issues in molecular biology · 2026Review
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Article
- Genome-wide chromatin recording resolves dynamic cell state changes.bioRxiv : the preprint server for biology · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Single cell analysis of developing Merkel cells reveals the emergence of non-coding RNA biotypes as a hallmark of terminal differentiation.Cell death and differentiation · 2026Article
- Structural, Compositional, and Dielectric State Profiling in Label-Free Single-Cell Monitoring.Small methods · 2026Review
- Synthetic developmental engineering of human liver organogenesis.Development (Cambridge, England) · 2026Review
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein.Nature communications · 2026Article
- The roles and heterogeneity of CD8International journal of molecular medicine · 2026Review
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Mapping of Neutrophils in Cancers: Insights From Spatial Omics Technologies.European journal of immunology · 2026Review
77 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
Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.
Indexed as
Identifiers
What Socratic 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 Socratic graph.