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ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks
- Source :
- Bioinformatics
- Publication Year :
- 2021
- Publisher :
- Cold Spring Harbor Laboratory, 2021.
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Abstract
- Motivation With 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. Results In 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.
- Subjects :
- Statistics and Probability
Sequence Analysis, RNA
Mechanism (biology)
business.industry
Computer science
Gene Expression Profiling
Deep learning
Genomics
High dimensional
Machine learning
computer.software_genre
Original Papers
Biochemistry
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Exome Sequencing
Cluster Analysis
Artificial intelligence
Single-Cell Analysis
business
Molecular Biology
computer
Generative adversarial network
Data integration
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....cb667634227f4cc50159df20ebc0cbf2