Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.

ABSTRACT: Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic sp

SUBMITTER: Yu Z 

PROVIDER: S-EPMC9877026 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature