ArticleNature biotechnology2019
A comparison of single-cell trajectory inference methods.
Article in Nature biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 971 papers.
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Who cites it
971 citing papers in PubMed.
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- A single-nucleus transcriptomic atlas of human inner ear development.Nature neuroscience · 2026Article
- Integrated Single Cell Analysis Reveals the Transcriptional Heterogeneity of Mouse Double Negative T Cells.International journal of molecular sciences · 2026Article
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- Gene-Chronos: parameter-efficient developmental time inference using a pretrained single-cell foundation model.Briefings in bioinformatics · 2026Article
- An integrated reference atlas of human skeletal muscle.EBioMedicine · 2026Article
- Advances in Poultry RNA-Omics Research: Technologies, RNA Information Layers, and Applications in Complex Traits.Animals : an open access journal from MDPI · 2026Review
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- Peripheral CD4 T-Cell State Remodeling in Migraine Revealed by Single-Cell RNA Sequencing.Journal of molecular neuroscience : MN · 2026Article
911 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline ( https://benchmark.dynverse.org ) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets.
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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 OpenQuestion graph.