A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder.

ABSTRACT: Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE), a dimensionality reduction method that preserves topological structure in scRNA-seq data. scGAE builds a cell graph and uses a multitask-oriented graph autoencoder to preserve topological structure information and feature information in scRNA-seq data simultaneously. We further extended scGAE for scRNA-seq data visualization, clustering, and trajectory inference. Analyses of simulated data showed that scGAE accurately reconstructs developmental trajectory and separates discrete cell cluste

SUBMITTER: Luo Z 

PROVIDER: S-EPMC8501122 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature