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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Veröffentlicht in:	BMC genomics Jg. 25; H. 1; S. 423 - 12
Hauptverfasser:	Zhang, Wei, Yu, Ruochen, Xu, Zeqi, Li, Junnan, Gao, Wenhao, Jiang, Mingfeng, Dai, Qi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 29.04.2024 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	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
Online-Zugang:	Volltext
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