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SPAE: Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq Data
Jiahao Yi1, Jiajia Liu2, Peng Guo1
1Bioinformatics and Biomedical Big Data Mining Laboratory, Department of Medical Informatics, School of Biology and Engineering, Guizhou Medical University, Anshun 561100, China.
Genomics, Proteomics & Bioinformatics
|July 29, 2026
Summary
We developed SPAE, a novel autoencoder model, to accurately characterize cell cycle dynamics in single-cell RNA sequencing data. This tool improves cell cycle analysis and facilitates the removal of cell cycle effects from gene expression data.
Area of Science:
- Genomics
- Computational Biology
- Molecular Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) offers insights into cellular heterogeneity and biological processes.
- Analyzing cell cycle dynamics in scRNA-seq data is challenging due to data complexity and subtle cell state differences.
Purpose of the Study:
- To develop an accurate and robust method for characterizing cell cycle dynamics and cell states in scRNA-seq data.
- To address the limitations of existing methods in analyzing cell cycle progression.
Main Methods:
- Development of the integrated Sinusoidal and Piecewise AutoEncoder (SPAE), an autoencoder-based piecewise linear model.
- Application of SPAE to scRNA-seq data for cell cycle characterization and effect removal.
Main Results:
- SPAE demonstrates significantly improved accuracy and robustness in cell cycle characterization compared to existing methods.
- SPAE accurately predicts cancer cell cycle transitions.
- SPAE effectively facilitates the removal of cell cycle effects from gene expression data.
Conclusions:
- SPAE provides a powerful new tool for analyzing cell cycle dynamics in scRNA-seq data.
- The method enhances the understanding of cellular heterogeneity and disease pathogenesis.
- SPAE is publicly available for non-commercial use.
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