ArticleNature biotechnology2024
PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes.
Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed.
- High-quantity capture of heterogeneous circulating tumor cellsMaterials today. Bio · 2026Article
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Hepatic Mechanisms and Therapeutic Target Underpinning Hypercoagulation in Obesity: Insights From Genetic and Transcriptomic Analyses.Exploration (Beijing, China) · 2026Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease.bioRxiv : the preprint server for biology · 2026Article
- Challenging the control through a single-cell perspective on normal adjacent tissue in colorectal cancer.iScience · 2026Article
- Charting single-cell lineages with synthetic and natural barcodes.Nature reviews. Genetics · 2026Review
- Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.Nature communications · 2026Article
- Gene regulatory networks orchestrating oocyte fate bifurcation in primordial follicles revealed by single-cell transcriptomics.Communications biology · 2026Article
- CRAK-Velo: chromatin accessibility kinetics integration improves RNA velocity estimation.Genome biology · 2026Article
- Benchmarking RNA velocity methods across 17 independent studies.Cell reports methods · 2026Article
- DestinyNet: A deep-learning framework for cell-fate analysis from lineage-tracing single-cell RNA sequencing data.Patterns (New York, N.Y.) · 2026Article
- VINE: Variational inference for scalable Bayesian reconstruction of species and cell-lineage phylogenies.bioRxiv : the preprint server for biology · 2026Article
- Interpretable learning of temporal cellular dynamics from single-cell data.Cell reports methods · 2026Article
- stVCR: spatiotemporal dynamics of single cells.Nature methods · 2026Article
- DNA methylation meets lineage tracing: History, recent progress, and future directions.Quantitative biology (Beijing, China) · 2026Article
- Single-cell mitochondrial lineage tracing: Opportunities and challenges.Quantitative biology (Beijing, China) · 2026Review
- Inferring cell differentiation maps from lineage tracing data.Nature methods · 2026Article
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11 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
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