A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale
A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale
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DOI：
10.1186/s12864-019-5787-x
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发表时间：
2019-05-22
期刊：
影响因子：
4.4
通讯作者：
Yu, Tianwei
中科院分区：
文献类型：
--
作者：
Kong, Yunchuan;Yu, Tianwei
背景生物调节系统是高度动态的。功能相关基因之间的相关性会随着不同的生物条件而变化，而这些变化在数据中通常是无法观察到的。在基因水平上，动态相关性导致三向基因相互作用，涉及改变相关性的一对基因和反映潜在细胞条件的第三个基因。这种类型的三元关系可以通过液体关联统计来量化。在基因三联体水平上研究这些三向相互作用揭示了生物系统中的重要调节机制。目前，由于高通量基因表达数据集中存在大量可能的三元组组合，因此没有方法可以在生物系统水平上检查三元关系并正式解决错误发现问题。结果在这里，我们提出了一种新方法——动态关联超图（HDC），来构建模块级三向交互网络。该方法能够呈现综合的统一超图来反映生物系统中的全局动态相关模式，为下游基因三联体水平分析提供指导。为了验证该方法的能力，我们使用癌症基因组图谱 (TCGA) 的黑色素瘤 RNA-seq 数据集和酵母细胞周期数据集进行了两次真实数据实验。由此产生的超图在生物学上显然是合理的，并提出了与数据中的生物学条件相关的新关系。结论我们相信，新方法提供了一种有价值的替代方法来分析组学数据，可以提取更高阶的结构。该软件位于 https://github.com/yuncuankong/HypergraphDynamicCorrelation。
BackgroundThe biological regulatory system is highly dynamic. Correlations between functionally related genes change over different biological conditions, which are often unobserved in the data. At the gene level, the dynamic correlations result in three-way gene interactions involving a pair of genes that change correlation, and a third gene that reflects the underlying cellular conditions. This type of ternary relation can be quantified by the Liquid Association statistic. Studying these three-way interactions at the gene triplet level have revealed important regulatory mechanisms in the biological system. Currently, due to the extremely large amount of possible combinations of triplets within a high-throughput gene expression dataset, no method is available to examine the ternary relationship at the biological system level and formally address the false discovery issue.ResultsHere we propose a new method, Hypergraph for Dynamic Correlation (HDC), to construct module-level three-way interaction networks. The method is able to present integrative uniform hypergraphs to reflect the global dynamic correlation pattern in the biological system, providing guidance to down-stream gene triplet-level analyses. To validate the method's ability, we conducted two real data experiments using a melanoma RNA-seq dataset from The Cancer Genome Atlas (TCGA) and a yeast cell cycle dataset. The resulting hypergraphs are clearly biologically plausible, and suggest novel relations relevant to the biological conditions in the data.ConclusionsWe believe the new approach provides a valuable alternative method to analyze omics data that can extract higher order structures. The software is at https://github.com/yunchuankong/HypergraphDynamicCorrelation.