Single-cell Co-expression Subnetwork Analysis.
Single-cell Co-expression Subnetwork Analysis.
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DOI：
10.1038/s41598-017-15525-z
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发表时间：
2017-11-08
影响因子：
4.6
通讯作者：
Diaz A
中科院分区：
文献类型：
--
作者：
Bartlett TE;Müller S;Diaz A
单细胞转录数据在基因组科学中迅速变得非常流行。基因组科学在使用网络模型来理解基因如何协同工作以执行特定生物功能方面也有着悠久的历史。然而，使用单一单元格数据带来了重大挑战，例如零通胀和技术噪音。这些挑战要求方法特别适用于单细胞数据的环境。最近，人们在发展统计网络模型背后的理论方面做出了很大努力。这导致了许多新模型的提出，并提供了对现有模型属性的透彻理解。然而，这方面的大量工作假设网络节点之间的二值关系，而基因组网络分析传统上是基于基因之间的连续值关联。在本文中，我们评估了几种已建立的基因组网络分析方法，比较了这些方法适用于单细胞环境的方式，并使用混合模型来推断基于基因-基因相关性的二元值关系。基于这些二元关系，我们发现使用网络统计学家流行的子网分析方法可以获得很好的结果。因此，这种方法允许检测这些单细胞基因组网络中的功能性子网络模块。
Single-cell transcriptomic data have rapidly become very popular in genomic science. Genomic science also has a long history of using network models to understand the way in which genes work together to carry out specific biological functions. However, working with single-cell data presents major challenges, such as zero inflation and technical noise. These challenges require methods to be specifically adapted to the context of single-cell data. Recently, much effort has been made to develop the theory behind statistical network models. This has lead to many new models being proposed, and has provided a thorough understanding of the properties of existing models. However, a large amount of this work assumes binary-valued relationships between network nodes, whereas genomic network analysis is traditionally based on continuous-valued correlations between genes. In this paper, we assess several established methods for genomic network analysis, we compare ways that these methods can be adapted to the single-cell context, and we use mixture-models to infer binary-valued relationships based on gene-gene correlations. Based on these binary relationships, we find that excellent results can be achieved by using subnetwork analysis methodology popular amongst network statisticians. This methodology thereby allows detection of functional subnetwork modules within these single-cell genomic networks.
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影响因子：
4.5
作者：
Johnstone, IM;Silverman, BW
通讯作者：
Silverman, BW
DOI：
10.1073/pnas.1507125112
发表时间：
2015-06-09
影响因子：
11.1
作者：
Darmanis S;Sloan SA;Zhang Y;Enge M;Caneda C;Shuer LM;Hayden Gephart MG;Barres BA;Quake SR
通讯作者：
Quake SR
影响因子：
4.5
作者：
Rohe, Karl;Chatterjee, Sourav;Yu, Bin
通讯作者：
Yu, Bin
影响因子：
4.4
作者：
Byrne K;Vuocolo T;Gondro C;White JD;Cockett NE;Hadfield T;Bidwell CA;Waddell JN;Tellam RL
通讯作者：
Tellam RL
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y