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.

中文翻译：

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单细胞多组学数据分析中基于张量分解的无监督特征提取

单细胞多组学数据集的分析是一个新颖的话题，并且具有相当大的挑战性，因为此类数据集包含大量具有大量缺失值的特征。在这项研究中，我们实施了一种最近提出的基于张量分解 (TD) 的无监督特征提取 (FE) 技术来解决这个难题。该技术可以成功整合由基因表达、DNA甲基化和可访问性组成的单细胞多组学数据。虽然最后两个维度很大，多达千万，只包含少数百分比的非零值，但基于 TD 的无监督 FE 可以集成三个组学数据集而无需填充缺失值。与将单细胞测量值嵌入二维空间时经常使用的 UMAP 一起，在集成单细胞组学数据集时，基于 TD 的无监督 FE 可以产生与分类一致的二维嵌入。基于 TD 无监督 FE 选择的基因也与合理的生物学作用显着相关。