Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany.
International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany.
BMC Bioinformatics. 2022 Apr 19;23(1):139. doi: 10.1186/s12859-022-04670-6.
With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.
We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software.
The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.
随着越来越多的（多组学）数据可用，从这些数据集中提取知识仍然是一个难题。经典的富集式分析需要预先定义的途径或基因集，这些途径或基因集被测试是否存在显著的失调，以评估该途径是否在研究的生物学过程中具有功能作用。从头开始识别这些途径可以减少预设途径或基因集固有的偏差。同时，从大型生物网络中从头开始定义和有效地识别这些途径是一个具有挑战性的问题。
我们提出了一种新的算法 DeRegNet，用于基于（多组学）数据中得出的失调分数，在有向图上识别最大失调的子网。DeRegNet 可以被解释为在给定特定的从头开始子图识别概率模型的情况下的最大似然估计。我们使用分数整数规划来解决由此产生的组合优化问题。我们可以证明，该方法在具有已知真实情况的模拟数据上优于相关算法。在一个公开的肝癌数据集上，我们可以证明 DeRegNet 可以识别适合患者分层的生物学上有意义的子网。DeRegNet 还可用于发现明确的多组学子图，我们通过展示具有一致甲基化-转录模式的子图来说明这一点。DeRegNet 作为开源软件免费提供。
所提出的算法框架及其可用的实现可以作为一种有价值的启发式假设生成工具，将组学数据置于生物分子网络的上下文中。
