scAI：一种用于平行单细胞转录组学和表观基因组学综合分析的无监督方法。
scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles.
机构信息
Department of Mathematics, University of California, Irvine, CA, 92697, USA.
The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA.
出版信息
Genome Biol. 2020 Feb 3;21(1):25. doi: 10.1186/s13059-020-1932-8.
Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms.
在同一个体细胞中同时测量转录组和表观基因组谱，为理解细胞命运提供了前所未有的机会。然而，缺乏对此类数据进行综合分析的有效方法。在这里，我们提出了一种单细胞聚集和整合（scAI）方法，以从平行转录组和表观基因组谱中去卷积细胞异质性。通过迭代学习，scAI 以无监督的方式聚集相似细胞中稀疏的表观基因组信号，从而与转录组测量结果进行一致融合。模拟研究和对三个真实数据集的应用证明了其在转录组和表观基因组层内剖析细胞异质性和理解转录调控机制的能力。
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