New York Genome Center, New York, NY, USA.
Center for Genomics and Systems Biology, New York University, New York, NY, USA.
Nat Rev Genet. 2019 May;20(5):257-272. doi: 10.1038/s41576-019-0093-7.
The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.
单细胞 RNA 测序（scRNA-seq）技术的最新发展与分析单个细胞中的遗传、表观遗传、空间、蛋白质组学和谱系信息的变革性新方法同时出现。这为整合方法提供了独特的机会，同时也带来了计算挑战，这些方法可以跨多种类型的数据进行共同学习。集成分析可以发现细胞模态之间的关系，学习细胞状态的整体表示，并能够汇集跨个体和技术产生的数据集。在这篇综述中，我们讨论了单细胞分辨率下不同类型数据的采集和整合的最新进展，重点是将基因表达数据与其他类型的单细胞测量进行整合。
