ArticleNature communications2021
Generalized and scalable trajectory inference in single-cell omics data with VIA.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Using In Silico Pseudobulk Differential Expression and Trajectory Analysis of Single Cell RNA Sequencing to Reveal Stem Cell State Transitions in Colonic Inflammation.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Application of human induced pluripotent stem cells for tissue modeling and therapy: are we on track?Stem cells (Dayton, Ohio) · 2026Review
- SnRNA-seq and genome-wide CRISPR screening define the complete transcriptional trajectory and functional drivers of podocyte stress.Cellular and molecular life sciences : CMLS · 2026Article
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling.bioRxiv : the preprint server for biology · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- R-loops shape the H2A.Z landscape and promote balanced lineage allocation during differentiation.Genome research · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectories.bioRxiv : the preprint server for biology · 2026Article
- Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.Nature communications · 2026Article
- Cardiac resident macrophages: the emerging role in arrhythmogenesis.Frontiers in immunology · 2026Review
- Soluble fermentable dietary fiber attenuates age-related cognitive impairment via neuroimmune and antioxidant modulation: evidence from multilevel analyses in populations and aging mouse models.Frontiers in immunology · 2026Article
- Towards time-resolved multiscale and multimodal imaging.Npj imaging · 2025Review
- Generalizable morphological profiling of cells by interpretable unsupervised learning.Nature communications · 2025Article
- YBX1 regulation of alveolar type II epithelial cells in idiopathic pulmonary fibrosis: mechanistic insights and small-molecule drug screening.Journal of translational medicine · 2025Article
- Article
- A semi-supervised Bayesian approach for marker gene trajectory inference from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2025Article
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- Joint representation and visualization of derailed cell states with Decipher.Genome biology · 2025Article
- Cerulenin partially corrects the disrupted developmental transcriptomic signature in Huntington's disease striatal medium spiny neurons.Stem cells (Dayton, Ohio) · 2025Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.
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