scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene–gene interactions

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 being...

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Vydáno v:	BMC genomics Ročník 25; číslo 1; s. 423 - 12
Hlavní autoři:	Zhang, Wei, Yu, Ruochen, Xu, Zeqi, Li, Junnan, Gao, Wenhao, Jiang, Mingfeng, Dai, Qi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 29.04.2024 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Animal Genetics and Genomics Artificial neural networks Biomedical and Life Sciences Cell differentiation Cells Cluster Analysis Clustering Coefficient compression Computational Biology - methods Cross correlation Data compression Deep Learning Differentiation (biology) Dual-channel self-attention mechanism Epistasis, Genetic Gene expression Gene Expression Profiling - methods Gene Regulatory Networks Gene sequencing Genes Genetic aspects Genetic research Humans Life Sciences Methods Microarrays Microbial Genetics and Genomics Molecular modelling Neural networks Neural Networks, Computer Plant Genetics and Genomics Probability distribution Proteomics Regression analysis RNA RNA sequencing Sequence Analysis, RNA - methods Single-Cell Analysis - methods Single-cell RNA sequencing (scRNA-seq) Static gene–gene interactions Temporal variations Topology Transcriptomics China Single-cell RNA sequencing (scRNA-seq) Static gene–gene interactions Dual-channel self-attention mechanism Coefficient compression
ISSN:	1471-2164, 1471-2164
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2164 1471-2164
DOI:	10.1186/s12864-024-10286-2