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scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene–gene interactions.
- Source :
-
BMC Genomics . 4/29/2024, Vol. 25 Issue 1, p1-12. 12p. - Publication Year :
- 2024
-
Abstract
- Background: Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and have outperformed statistic approaches in cell clustering. Results: Using cross-correlation to capture gene–gene interactions, this study proposes the scCompressSA method to integrate topological patterns from scRNA-seq data, with support of self-attention (SA) based coefficient compression (CC) block. This SA-based CC block is able to extract and employ static gene–gene interactions from scRNA-seq data. This proposed scCompressSA method has enhanced clustering accuracy in multiple benchmark scRNA-seq datasets by integrating topological and temporal features. Conclusion: Static gene–gene interactions have been extracted as temporal features to boost clustering performance in single-cell clustering For the scCompressSA method, dual-channel SA based CC block is able to integrate topological features and has exhibited extraordinary detection accuracy compared with previous clustering approaches that only employ temporal patterns. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14712164
- Volume :
- 25
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Genomics
- Publication Type :
- Academic Journal
- Accession number :
- 177003620
- Full Text :
- https://doi.org/10.1186/s12864-024-10286-2