ArticleNature communications2023
Benchmarking integration of single-cell differential expression.
Article in Nature communications, 2023. 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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Who cites it
46 citing papers in PubMed.
- Cell DiffErential Expression by Pooling (CellDEEP) highlights issues in differential gene expression in scRNA-seq.NAR genomics and bioinformatics · 2026Article
- Effects of semaglutide on kidney disease in type 2 diabetes: a randomized placebo-controlled trial.Nature medicine · 2026Article
- HopRatio: Profiling single-sample transcriptomic dysregulation using stable gene-ordering relationships.iScience · 2026Article
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- Anemoside B4 upregulates DDAH2 and attenuates oxidative stress induced endothelial dysfunction.Molecular and cellular biochemistry · 2026Article
- Coordinated dysregulation of modular gene activity in human neuropathologies.bioRxiv : the preprint server for biology · 2026Article
- Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients.Nature communications · 2026Article
- Single-cell and spatial transcriptomics in Phragmites australis reveal the association of B chromosomes with plant invasiveness.Genome biology · 2026Article
- Structure-preserving multivariate hypothesis testing for mass spectrometry imaging and single-cell data.Bioinformatics (Oxford, England) · 2026Article
- An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease.Genome biology · 2026Article
- Article
- A highly resolved integrated single-cell atlas of HPV-negative head and neck cancer.Communications medicine · 2026Article
- scGPT: end-to-end protocol for fine-tuned retinal cell type annotation.Nature protocols · 2026Review
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.Briefings in bioinformatics · 2026Article
- DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data.bioRxiv : the preprint server for biology · 2026Article
- FLASH-MM: fast and scalable single-cell differential expression analysis using linear mixed-effects models.Nature communications · 2026Article
- Metabolic reprogramming-associated genomic instability drives colorectal cancer progression via the UBXN1-NF-κB axis.American journal of translational research · 2026Article
- GxP-Ready Single-Cell RNA-seq and Spatial Transcriptomics End-to-End Pipeline for Clinical Research.Computational and structural biotechnology journal · 2026Article
- Reference-signature projection framework for exercise transcriptomic directionality based on the IAV-DI reference axis.Frontiers in physiology · 2026Article
- A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch.Frontiers in microbiology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes.
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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.