PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 41117775

PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.

Yuheng Lai·Dongyuan Song·Lucy Xia·Jingyi Jessica Li
View original source ↗Export metadata ↓

Abstract

SUMMARY: Identifying differentially expressed (DE) genes along cell pseudotime is crucial for understanding dynamic biological processes captured by single-cell RNA sequencing. However, existing DE methods either produce invalid P-values by ignoring the uncertainty in pseudotime inference or struggle to scale with the growing size of modern datasets. To address these limitations, we introduce PseudotimeDE-fast, a scalable method for detecting DE genes along pseudotime with well-calibrated P-values. Through comprehensive simulations and real-data analyses, we demonstrate that PseudotimeDE-fast delivers comparable or superior performance to existing approaches while offering substantial improvements in computational efficiency. AVAILABILITY AND IMPLEMENTATION: PseudotimeDE-fast is implemented in R with Rcpp acceleration and released under the MIT license. The source code is available at: https://github.com/dsong-lab/PseudotimeDE.

Explore related subjects

Single-Cell AnalysisSoftwareGene Expression ProfilingSequence Analysis, RNAHumansAlgorithmsComputational BiologyJournal ArticleResearch Support, N.I.H., Extramural

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuheng Lai, Dongyuan Song, Lucy Xia, Jingyi Jessica Li. 2025-11-01. PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.. https://doi.org/10.1093/bioinformatics%2Fbtaf573

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository.

Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata. Here, we introduce scBaseCount, a database that leverages an AI agent to automate discovery and metadata extraction and standardize data processing. Built by mining all 10x Genomics datasets, scBaseCount is the largest public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues. It offers an unbiased view of the data landscape within the SRA and enables the training of more performant computational models through access to broader phenotypic diversity. Uniform processing enables measurement of both intronic and exonic reads and non-coding gene expression and improves alignment across experiments. Moreover, scBaseCount provides a blueprint for how AI can be leveraged to autonomously curate biological data repositories.

Single-Cell Analysis↗
PubMed2026

scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.

Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.

Single-Cell Analysis↗
PubMed2026

Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.

MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. RESULTS: We present a new approach, MetaFR, to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. AVAILABILITY AND IMPLEMENTATION: MetaFR is available under https://github.com/SchulzLab/MetaFR.

Single-Cell Analysis↗