Multi-omics integration in the age of million single-cell data.
Multi-omics integration in the age of million single-cell data.
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DOI：
10.1038/s41581-021-00463-x
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发表时间：
2021-11
期刊：
影响因子：
--
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单细胞技术的爆炸式发展揭示了以前未被充分认识的细胞类型的异质性以及与性别、疾病、发育和其他过程相关的新细胞状态。从转录组分析开始，单细胞技术已经扩展到多组学方法，现在可以同时测量数据模式和细胞空间背景。现在可以获得数百万个细胞、全基因组测量和多种模式的数据。虽然这种多模态数据集的分析有可能提供新的见解，不能推断出与一个单一的分析模式的生物过程，非常大的，复杂的，多模态数据集成到生物模型和机制代表了相当大的挑战。需要了解数据集成和可视化方法的原理，以确定哪些方法最适合于特定的单细胞数据集。每一类方法在实现各种生物学目标的能力方面都有优势和缺陷，包括细胞类型分类，调控网络建模和生物过程推断。在选择数据集成策略时，必须考虑多组数据是否匹配（即在同一细胞上测量）或不匹配（即在不同细胞上测量），更重要的是，集成分析的整体建模和可视化目标。
An explosion in single cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single cell techniques have been extended to multi-omics approaches, and now enable the simultaneous measurement of data modalities and cellular spatial context. Data are now available for millions of cells, for whole-genome measurements, and for multiple modalities. Although analyses of such multimodal datasets have potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single cell data set. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell type classification, regulatory network modeling, and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multiome data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.
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影响因子：
64.8
作者：
Cao, Junyue;Spielmann, Malte;Shendure, Jay
通讯作者：
Shendure, Jay
影响因子：
5.9
作者：
Andersson A;Bergenstråhle J;Asp M;Bergenstråhle L;Jurek A;Fernández Navarro J;Lundeberg J
通讯作者：
Lundeberg J
影响因子：
9.5
作者：
Han, Syung Hun;Choi, Yongwon;Lee, Daeyeon
通讯作者：
Lee, Daeyeon
DOI：
10.1126/science.aau0730
发表时间：
2018-09-28
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Cao J;Cusanovich DA;Ramani V;Aghamirzaie D;Pliner HA;Hill AJ;Daza RM;McFaline-Figueroa JL;Packer JS;Christiansen L;Steemers FJ;Adey AC;Trapnell C;Shendure J
通讯作者：
Shendure J
深度测序揭示了单细胞转录组变化的细胞类型特异性模式。
影响因子：
12.3
作者：
Dueck H;Khaladkar M;Kim TK;Spaethling JM;Francis C;Suresh S;Fisher SA;Seale P;Beck SG;Bartfai T;Kuhn B;Eberwine J;Kim J
通讯作者：
Kim J