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bioRxiv · 10.1101/2021.08.25.457731

Tensor-decomposition--based unsupervised feature extraction in single-cell multiomics data analysis

Taguchi, Y.-h.·Turki, T.
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Abstract

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 percentages of non-zero values, TD-based unsupervised FE can integrate three omics datasets without filling 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 were also significantly related to reasonable biological roles.

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Taguchi, Y.-h., Turki, T.. 2021-08-28. Tensor-decomposition--based unsupervised feature extraction in single-cell multiomics data analysis. https://doi.org/10.1101/2021.08.25.457731

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