ArticleGenome biology2021
PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 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
- The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime.Nucleic acids research · 2026Article
- Single-cell trajectory inference for detecting transient events in biological processes.Nucleic acids research · 2026Article
- PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.PLoS computational biology · 2026Article
- mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data.Nature communications · 2026Article
- scRDiT: Generating Single-cell RNA-seq Data by Diffusion Transformers and Accelerating Sampling.Interdisciplinary sciences, computational life sciences · 2026Article
- PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.Genome biology · 2026Article
- Interpretable trajectory inference with single-cell linear adaptive negative-binomial expression (scLANE) testing.Nucleic acids research · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Article
- Pseudotimecascade Visualizes Gene Expression Cascade in Pseudotime Analysis.Computational and structural biotechnology journal · 2026Article
- Tumor prognostic risk stratification based on pseudo-time analysis of single-cell sequencing for patients with lung adenocarcinoma.Journal of thoracic disease · 2025Article
- Building dynamical models of multi-step state transitions from single cell gene expression trajectories.bioRxiv : the preprint server for biology · 2025Article
- Pseudotimecascade visualizes gene expression cascade in pseudotime analysis.bioRxiv : the preprint server for biology · 2025Article
- scFPC-DE: Robust Differential Expression Analysis Along Single Cell Trajectories via Functional Principal Component Analysis.bioRxiv : the preprint server for biology · 2025Article
- PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.Bioinformatics (Oxford, England) · 2025Article
- FAPNature communications · 2025Article
- Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE.Cell systems · 2025Article
- Interpretable Trajectory Inference with Single Cell Linear Adaptive Negative-binomial Expression (scLANE) Testing.bioRxiv : the preprint server for biology · 2025Article
- Gas1-high quiescent neural stem cells are multipotent and produce oligodendrocytes during aging and after demyelinating injury.PLoS biology · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.
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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 Socratic graph.