Massive-scale gene co-expression network construction and robustness testing using random matrix theory.
Massive-scale gene co-expression network construction and robustness testing using random matrix theory.
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DOI：
10.1371/journal.pone.0055871
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Smith MC
中科院分区：
文献类型：
--
作者：
Gibson SM;Ficklin SP;Isaacson S;Luo F;Feltus FA;Smith MC
基因关系及其对生物学功能和表型的影响是系统生物学研究的重点。利用微阵列表达谱构建的基因共表达网络是发现和解释基因关系的一种技术。一个知识无关的阈值技术，如随机矩阵理论（RMT），是有用的识别有意义的关系。然后将阈值网络中高度连接的基因分组为模块，以提供对其集体功能的洞察。虽然已经表明共表达网络是生物相关的，但还没有确定在输入样本集中给定扰动的情况下，任何给定网络在功能上稳健到什么程度。对于这样的测试，需要数百个网络，因此需要快速构建这些网络的工具。为了研究具有不同输入的网络的功能鲁棒性，我们增强了现有的RMT实现以提高可扩展性，并测试了人类（智人），水稻（水稻）和芽殖酵母（酿酒酵母）的功能鲁棒性。我们证明了网络构建时间和计算需求的显着减少，并表明尽管网络之间的全局属性存在一些变化，但功能相似性仍然很高。此外，由RMT阈值化的共表达网络捕获的生物功能是高度鲁棒的。
The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.
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影响因子：
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