ScMAE: a masked autoencoder for single-cell RNA-seq clustering.

ABSTRACT:

Motivation

Single-cell RNA sequencing has emerged as a powerful technology for studying gene expression at the individual cell level. Clustering individual cells into distinct subpopulations is fundamental in scRNA-seq data analysis, facilitating the identification of cell types and exploration of cellular heterogeneity. Despite the recent development of many deep learning-based single-cell clustering methods, few have effectively exploited the correlations among genes, resulting in suboptimal clustering outcomes.

Results

Here, we propose a novel masked autoencoder-based method, scMAE, for cell clustering. scMAE perturbs gene expression and employs a masked autoencoder to reconstruct the original data, learning robust and informative cell representations. The masked autoenco

SUBMITTER: Fang Z 

PROVIDER: S-EPMC10832357 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature