Computational methods for single-cell omics across modalities.,Nature Methods

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Computational methods for single-cell omics across modalities.
Nature Methods ( IF 36.1 ) Pub Date : 2020-01-01 , DOI: 10.1038/s41592-019-0692-4
Mirjana Efremova ₁ , Sarah A Teichmann _{1,

2}

Affiliation

Single-cell omics approaches provide high-resolution data on cellular phenotypes, developmental dynamics and communication networks in diverse tissues and conditions. Emerging technologies now measure different modalities of individual cells, such as genomes, epigenomes, transcriptomes and proteomes, in addition to spatial profiling. Combined with analytical approaches, these data open new avenues for accurate reconstruction of gene-regulatory and signaling networks driving cellular identity and function. Here we summarize computational methods for analysis and integration of single-cell omics data across different modalities and discuss their applications, challenges and future directions.

中文翻译：

跨模式的单细胞组学的计算方法。

单细胞组学方法可提供有关不同组织和条件下细胞表型，发育动力学和通信网络的高分辨率数据。现在，除了空间分析外，新兴技术还可以测量单个细胞的不同模式，例如基因组，表观基因组，转录组和蛋白质组。结合分析方法，这些数据为精确重建驱动细胞身份和功能的基因调控和信号网络开辟了新途径。在这里，我们总结了用于分析和整合不同模式下的单细胞组学数据的计算方法，并讨论了它们的应用，挑战和未来方向。

更新日期：2020-01-06

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