ArticleNature communications2020
Trajectory-based differential expression analysis for single-cell sequencing data.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 488 papers, 1 of them a synthesis that pooled it.
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Who cites it
488 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Depletion of Effector Regulatory T Cells Associates with Major Response to Induction Dual Immune Checkpoint Blockade.Cancer discovery · 2025Trial
- 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
- Progressive suppression of regulated cell death defines terminal macrophage states in liver cirrhosis.Annals of medicine · 2026Article
- Diverse infections transcriptionally reprogram the intestinal epithelium and epithelial-immune cell interactions.Nature immunology · 2026Article
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- Immune Hallmarks of Recurrent Immune Checkpoint Inhibitor-Mediated Inflammatory Arthritis.Cancer immunology research · 2026Article
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
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- A Human Accelerated Region Drives Opposing Heterochronic Changes in Craniofacial and Limb Development.bioRxiv : the preprint server for biology · 2026Article
428 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.
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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 OpenQuestion graph.