ArticleNature methods2025
Gene-level alignment of single-cell trajectories.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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
22 citing papers in PubMed.
- Article
- Spatial Logic and Evolutionary Innovation in Human Placentation.bioRxiv : the preprint server for biology · 2026Article
- Article
- An integrated reference atlas of human skeletal muscle.EBioMedicine · 2026Article
- Single-nucleus analysis of the adult human olfactory epithelium uncovers shared neurogenesis programs with the brain.Nature communications · 2026Article
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- Single-nucleus multi-omics dissection of dysregulated trophoblast development and disrupted immune microenvironment in complete hydatidiform moles.Science China. Life sciences · 2026Article
- Spatial transcriptomics reveals coordinated ventricular patterning and maturation in the developing human heart.Nature communications · 2026Article
- Distinct stem cell identities converge into shared erythroid stress in ERCC6L2 disease and Shwachman-Diamond syndrome.HemaSphere · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Role of SCAP in regulation of pancreatic homeostasis, pancreatitis, and tumorigenesis.Oncogene · 2026Article
- Single-nucleus analysis reveals human-specific oligodendrocyte polarization and conserved neuronal responses after severe traumatic brain injury.Nature communications · 2026Article
- Cross-species insights into placental evolution and diseases at the single-cell resolution.Nature communications · 2026Article
- Integrator subunit IntS11 orchestrates the temporal dynamics of neural lineage progression in Drosophila.Cell & bioscience · 2026Article
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
- Cross-species identification of conserved cell-type specific mechanisms during early placenta development in ruminants.Scientific reports · 2025Article
- Cross-species comparison reveals therapeutic vulnerabilities halting glioblastoma progression.Nature communications · 2025Article
- Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025Review
- TrAGEDy-trajectory alignment of gene expression dynamics.Bioinformatics (Oxford, England) · 2025Article
- scLTNN: an innovative tool for automatically visualizing single-cell trajectories.Bioinformatics advances · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.
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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 Socratic graph.