Tritchler David, Towle-Miller Lorin M, Miecznikowski Jeffrey C
Department of Biostatistics, University at Buffalo, Buffalo, NY 14260, USA.
Biostatistics Division, University of Toronto, Toronto, ON M5S 1A1, Canada.
Bioinform Adv. 2021 Sep 16;1(1):vbab018. doi: 10.1093/bioadv/vbab018. eCollection 2021.
High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion. Of special interest are functional modules, cooperating sets of variables affecting the target which can be characterized by a graph. In applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. This property requires that the module variables have a joint effect on the target outcome with no internal conflict, an efficiency that may be applied to biological networks.
In this paper, we model genomic variables in signed undirected graphs for applications where the set of predictor variables influences an outcome. Consequences of the balance property are exploited to implement a new module discovery algorithm, balanced Functional Module Detection (bFMD), which selects a subset of variables from high-dimensional data that compose a balanced functional module. Our bFMD algorithm performed favorably in simulations as compared to other module detection methods. Additionally, bFMD detected interpretable results in an application using RNA-seq data obtained from subjects with Uterine Corpus Endometrial Carcinoma using the percentage of tumor invasion as the outcome of interest. The variables selected by bFMD have improved interpretability due to the logical consistency afforded by the balance property.
Supplementary data are available at online.
高维基因组数据可用于分析变量对诸如临床结果等目标变量的影响。为了理解影响目标的潜在生物学机制，发现完整的相关变量集至关重要。因此，变量选择是一个主要目标，这与预测标准不同。特别令人感兴趣的是功能模块，即影响目标的变量协作集，可用图来表征。在社交网络等应用中，无向带符号图中的平衡概念表征了网络内关联的一致性。此属性要求模块变量对目标结果具有联合效应且无内部冲突，这种效率可应用于生物网络。
在本文中，我们为预测变量集影响结果的应用在带符号无向图中对基因组变量进行建模。利用平衡属性的结果来实现一种新的模块发现算法，即平衡功能模块检测（bFMD），该算法从高维数据中选择构成平衡功能模块的变量子集。与其他模块检测方法相比，我们的bFMD算法在模拟中表现良好。此外，bFMD在一项应用中使用从子宫体子宫内膜癌患者获得的RNA测序数据，以肿瘤浸润百分比作为感兴趣的结果，检测到了可解释的结果。由于平衡属性提供的逻辑一致性，bFMD选择的变量具有更高的可解释性。
补充数据可在网上获取。
