scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 217 - 17
• Main Authors: Wang, Zile, Wang, Haiyun, Zhao, Jianping, Zheng, Chunhou
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.05.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Adversarial autoencoder; Algorithms; Analysis; Bioinformatics; Biomedical and Life Sciences; Cluster Analysis; Clustering; Clusters (Chemistry); Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Datasets; Deep learning; Gene expression; Gene Expression Profiling; Gene sequencing; Genomics; Health aspects; Life Sciences; Microarrays; Neural networks; Ribonucleic acid; RNA; RNA sequencing; scRNA-seq; Semi-supervised; Sequence analysis; Sequence Analysis, RNA; Single-Cell Analysis; Single-Cell Gene Expression Analysis; Transcriptome; Transcriptomes; China; Deep learning; scRNA-seq; Clustering; Adversarial autoencoder; Semi-supervised
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05339-4

Background Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging. Results We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses. Conclusion scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE .