Newman Nolan K, Macovsky Matthew, Rodrigues Richard R, Bruce Amanda M, Pederson Jacob W, Patil Sankalp S, Padiadpu Jyothi, Dzutsev Amiran K, Shulzhenko Natalia, Trinchieri Giorgio, Brown Kevin, Morgun Andrey
College of Pharmacy, Oregon State University, Corvallis, OR, USA.
Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
bioRxiv. 2023 Mar 29:2023.02.22.529449. doi: 10.1101/2023.02.22.529449.
Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the network's local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.
技术进步产生了大量的高通量组学数据。整合来自多个队列以及新的和先前发表的研究中的不同组学类型的数据，可以提供生物系统的整体视图，并有助于解读其关键参与者和关键机制。在本方案中，我们描述了如何使用跨界网络分析（TkNA），这是一个独特的因果推断分析框架，可对队列进行荟萃分析，并在控制宿主-微生物群（或任何多组学数据）在特定病症或疾病中的病理或生理反应的测量参数中检测主调控因子。TkNA首先重建代表统计模型的网络，该模型捕捉生物系统不同组学之间的复杂关系。在此，它通过识别多个队列中倍数变化方向和相关符号的稳健且可重复的模式来选择差异特征及其组内相关性。接下来，使用因果关系敏感指标、统计阈值和一组拓扑标准来选择形成跨界网络的最终边。分析的第二部分涉及对网络进行探究。利用网络的局部和全局拓扑指标，它检测负责控制给定子网或控制界别和/或子网之间通信的节点。TkNA方法的潜在基础涉及包括因果律、图论和信息论在内的基本原理。因此，TkNA可通过对任何宿主和/或微生物群多组学数据进行网络分析来用于因果推断。这个快速且易于运行的方案只需要对Unix命令行环境有非常基本的了解。
