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scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.
- Source :
- Briefings in Bioinformatics; Sep2024, Vol. 25 Issue 5, p1-13, 13p
- Publication Year :
- 2024
-
Abstract
- Single-cell RNA sequencing (scRNA-seq) enables the exploration of biological heterogeneity among different cell types within tissues at a resolution. Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels. To address this, we have designed a novel cell type inference method for scRNA-seq data, termed scLEGA. scLEGA employs a novel zero-inflated negative binomial (ZINB) loss function that fully considers the contribution of genes with lower expression levels and combines two distinct scRNA-seq clustering strategies through a multi-head attention mechanism. It utilizes a low-expression optimized denoising autoencoder, based on the novel ZINB model, to extract low-dimensional features and handle dropout events, and a GCN-based graph autoencoder (GAE) that leverages neighbor information to guide dimensionality reduction. The iterative fusion of denoising and topological embedding in scLEGA facilitates the acquisition of cluster-friendly cell representations in the hidden embedding, where similar cells are brought closer together. Compared to 12 state-of-the-art cell type inference methods on 15 scRNA-seq datasets, scLEGA demonstrates superior performance in clustering accuracy, scalability, and stability. Our scLEGA model codes are freely available at https://github.com/Masonze/scLEGA-main. [ABSTRACT FROM AUTHOR]
- Subjects :
- RNA sequencing
SCALABILITY
GENES
HETEROGENEITY
TISSUES
Subjects
Details
- Language :
- English
- ISSN :
- 14675463
- Volume :
- 25
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Briefings in Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 179874076
- Full Text :
- https://doi.org/10.1093/bib/bbae371