scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data.
Methods
; 229: 115-124, 2024 Sep.
Article
in En
| MEDLINE
| ID: mdl-38950719
ABSTRACT
Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at https//github.com/labiip/scGAAC.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sequence Analysis, RNA
/
Single-Cell Analysis
Limits:
Humans
Language:
En
Journal:
Methods
Journal subject:
BIOQUIMICA
Year:
2024
Type:
Article
Affiliation country:
China