Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis

• Main Authors: Y-h. Taguchi, Turki Turki
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Genes
• Subjects: tensor decomposition; feature extraction; single-cell; multiomics data
• Online Access: https://www.mdpi.com/2073-4425/12/9/1442

Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentage of nonzero values, TD-based unsupervised FE can integrate three omics datasets without filling in missing values. Together with UMAP, which is used frequently when embedding single-cell measurements into two-dimensional space, TD-based unsupervised FE can produce two-dimensional embedding coincident with classification when integrating single-cell omics datasets. Genes selected based on TD-based unsupervised FE are also significantly related to reasonable biological roles.