IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac466.
Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.
基于基因的转录组分析，如差异表达分析，可以识别导致疾病发生、细胞分化和其他生物过程的关键因素。然而，这还不够，因为基本的生命活动主要是由基因之间的相互作用驱动的。尽管已经有许多用于识别差异基因相互作用的差异网络推断方法，但目前大多数研究仍然只使用网络节点的信息进行下游分析。为了深入研究差异基因相互作用，我们应该在获得差异网络后进行基于相互作用的转录组分析（IBTA）。在本文中，我们通过开发 Co-hub 差异网络推断（CDN）算法和一种新的基于相互作用的度量指标 pivot APC2，说明了 IBTA 的工作流程。通过与其他流行的差异网络推断算法的模拟实验比较，我们验证了 CDN 的优越性能。此外，使用结直肠癌、COVID-19 和三阴性乳腺癌数据集进行了三个案例研究，以证明我们基于相互作用的分析过程发现因果机制的能力。
