ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks.

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 benchmark

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9364370 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature