ArticleBioinformatics (Oxford, England)2025
TrAGEDy-trajectory alignment of gene expression dynamics.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- BLASE: bulk linkage analysis for single cell experiments - teasing out the secrets of bulk transcriptomics with trajectory analysis.BMC methods · 2026Article
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling.bioRxiv : the preprint server for biology · 2026Article
- The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime.Nucleic acids research · 2026Article
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
- Cross-species comparison reveals therapeutic vulnerabilities halting glioblastoma progression.Nature communications · 2025Article
- Distinct gene regulatory dynamics drive skeletogenic cell fate convergence during vertebrate embryogenesis.Nature communications · 2025Article
- Gene-level alignment of single-cell trajectories.Nature methods · 2025Article
- Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
motivationSingle-cell transcriptomics sequencing is used to compare different biological processes. However, often, those processes are asymmetric which are difficult to integrate. Current approaches often rely on integrating samples from each condition before either cluster-based comparisons or analysis of an inferred shared trajectory.
resultsWe present Trajectory Alignment of Gene Expression Dynamics (TrAGEDy), which allows the alignment of independent trajectories to avoid the need for error-prone integration steps. Across simulated datasets, TrAGEDy returns the correct underlying alignment of the datasets, outperforming current tools which fail to capture the complexity of asymmetric alignments. When applied to real datasets, TrAGEDy captures more biologically relevant genes and processes, which other differential expression methods fail to detect when looking at the developments of T cells and the bloodstream forms of Trypanosoma brucei when affected by genetic knockouts. AVAILABILITY AND IMPLEMENTATION: TrAGEDy is freely available at https://github.com/No2Ross/TrAGEDy, and implemented in R.
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