Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning.

ABSTRACT: The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-scale CNS scRNA-seq data still encounters challenges due to the complexity and heterogeneity among CNS cell types/subtypes. In this study, we introduce a self-supervised contrastive learning method, called scCM, for integrating large-scale CNS scRNA-seq data. scCM brings functionally related cells close together while simultaneously pushing apart dissimilar cells by comparing the variations of gene expression, effectively revealing the heterogeneous relationships within the CNS cell types/subtypes. The effectiveness of scCM is evaluated on 20 C

SUBMITTER: Fang Y 

PROVIDER: S-EPMC11383967 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature