ArticleBioinformatics (Oxford, England)2022
ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.Briefings in bioinformatics · 2026Article
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios.PLoS computational biology · 2026Article
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- An order-preserving batch-effect correction method based on a monotonic deep learning framework.Briefings in bioinformatics · 2025Article
- Partially characterized topology guides reliable anchor-free scRNA-integration.Communications biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis.Briefings in bioinformatics · 2024Article
- A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization.PLoS computational biology · 2024Article
- scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.Genome biology · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
motivationWith the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However, integrative downstream analysis from multiple scRNA-seq datasets remains challenging due to batch effects.
resultsIn this article, we propose a light-structured deep learning framework called ResPAN for scRNA-seq data integration. ResPAN is based on Wasserstein Generative Adversarial Network (WGAN) combined with random walk mutual nearest neighbor pairing and fully skip-connected autoencoders to reduce the differences among batches. We also discuss the limitations of existing methods and demonstrate the advantages of our model over seven other methods through extensive benchmarking studies on both simulated data under various scenarios and real datasets across different scales. Our model achieves leading performance on both batch correction and biological information conservation and maintains scalable to datasets with over half a million cells. AVAILABILITY AND IMPLEMENTATION: An open-source implementation of ResPAN and scripts to reproduce the results can be downloaded from: https://github.com/AprilYuge/ResPAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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