Multi-omic single-cell technologies, which simultaneously measure the transcriptional and epigenomic state of the same cell, enable understanding epigenetic mechanisms of gene regulation. However, noisy and sparse data pose fundamental statistical challenges to extract biological knowledge from complex datasets. SHARE-Topic, a Bayesian generative model of multi-omic single cell data using topic models, aims to address these challenges. SHARE-Topic identifies common patterns of co-variation between different omic layers, providing interpretable explanations for the data complexity. Tested on data from different technological platforms, SHARE-Topic provides low dimensional representations recapitulating known biology and defines associations between genes and distal regulators in individual cells.

中文翻译：

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分享主题：单细胞多组学数据的贝叶斯可解释建模

多组学单细胞技术可同时测量同一细胞的转录和表观基因组状态，有助于了解基因调控的表观遗传机制。然而，嘈杂和稀疏的数据对从复杂数据集中提取生物知识提出了基本的统计挑战。SHARE-Topic 是一种使用主题模型的多组学单细胞数据的贝叶斯生成模型，旨在解决这些挑战。SHARE-Topic 识别不同组学层之间共变的常见模式，为数据复杂性提供可解释的解释。SHARE-Topic 对来自不同技术平台的数据进行了测试，提供了概括已知生物学的低维表示，并定义了单个细胞中基因和远端调节因子之间的关联。