ScSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.

Results

We propose the SMD deep learning model, which integrates nonlinear dimensionality reduction techniques with a porous dilated attention gate component. Built upon a convolutional autoencoder and informed by the negative binomial distribution, the SMD model efficiently captures essential cell clustering features and dynamically adjusts feature

SUBMITTER: Cui X 

PROVIDER: S-EPMC11780796 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature