scGAMNN: Graph Antoencoder-Based Single-Cell RNA Sequencing Data Integration Algorithm Using Mutual Nearest Neighbors.

Researchers

Journal

Modalities

Models

Abstract

It is critical to correctly assemble high-dimensional single-cell RNA sequencing (scRNA-seq) datasets and downscale them for downstream analysis. However, given the complex relationships between cells, it remains a challenge to simultaneously eliminate batch ef-fects between datasets and maintain the topology between cells within each dataset. Here, we propose scGAMNN, a deep learning model based on graph autoencoder, to simultaneously achieve batch correction and topology-preserving dimensionality reduction. The low-dimensional integrated data obtained by scGAMNN can be used for visu-alization, clustering and trajectory inference.By comparing it with the other five methods, multiple tasks show that scGAMNN consistently has comparable data integration performance in clustering and trajectory conservation.