University of Florida, Department of Biostatistics, Gainesville, 32611, USA.
University of Cincinnati, Department of Pediatrics, Cincinnati, 45229, USA.
Sci Rep. 2019 Apr 2;9(1):5479. doi: 10.1038/s41598-019-41918-3.
The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. Research in systems biology has taken advantage of these opportunities by gleaning insights into gene regulatory networks through the analysis of gene association networks. Contrasting networks from different populations can reveal the many different roles genes fill, which can lead to new discoveries in gene function. Pathologies can also arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis between two populations is proposed. The framework importantly allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differentially connected genes when incorporating known pathways, even if the pathway knowledge is partially inaccurate. Another simulation study compares the general framework with four state-of-the-art methods. Two RNA-seq datasets are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.
下一代测序的出现为分析基因表达数据带来了新的机会。系统生物学研究通过分析基因关联网络，从基因调控网络中收集到了深入的见解，从而利用了这些机会。对比不同群体的网络可以揭示基因所扮演的许多不同角色，从而在基因功能方面带来新的发现。这些基因-基因相互作用的异常也可能导致病理学的发生。揭示这些网络异常为理解和治疗疾病提供了一条新途径。本文提出了一种将已知基因调控途径整合到两个群体之间差异网络分析中的通用框架。该框架重要的是允许使用任何基因-基因关联度量，并通过置换检验进行推断。一项模拟研究调查了在纳入已知途径时识别差异连接基因的性能，即使途径知识部分不准确。另一项模拟研究将通用框架与四种最先进的方法进行了比较。分析了两个 RNA-seq 数据集，以说明该框架在实践中的应用。在这两个例子中，分析揭示了已知具有生物学意义的基因和途径，以及可能用于激发未来研究的潜在新发现。
