ScGCL: an imputation method for scRNA-seq data based on graph contrastive learning.

ABSTRACT:

Motivation

Single-cell RNA-sequencing (scRNA-seq) is widely used to reveal cellular heterogeneity, complex disease mechanisms and cell differentiation processes. Due to high sparsity and complex gene expression patterns, scRNA-seq data present a large number of dropout events, affecting downstream tasks such as cell clustering and pseudo-time analysis. Restoring the expression levels of genes is essential for reducing technical noise and facilitating downstream analysis. However, existing scRNA-seq data imputation methods ignore the topological structure information of scRNA-seq data and cannot comprehensively utilize the relationships between cells.

Results

Here, we propose a single-cell Graph Contrastive Learning method for scRNA-seq data imputation, named scGCL, which int

SUBMITTER: Xiong Z 

PROVIDER: S-EPMC9991516 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature