Medical Scientist Training Program, University of Rochester, Rochester, New York, United States of America.
Biophysics, Structural, and Computational Biology Program, University of Rochester, Rochester, New York, United States of America.
PLoS Comput Biol. 2019 Sep 3;15(9):e1007317. doi: 10.1371/journal.pcbi.1007317. eCollection 2019 Sep.
Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data. However, most existing methods do not consider signal integration represented by pathway topology, resulting in enrichment of convergent pathways when downstream genes are modulated. Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes. This implementation can be facilitated for large-scale data by discrete state network modeling due to simplicity in parameterization. Here, we model cellular heterogeneity using discrete state dynamics and measure pathway activities in cross-sectional data. We introduce a new algorithm, Boolean Omics Network Invariant-Time Analysis (BONITA), for signal propagation, signal integration, and pathway analysis. Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity, and propagates binary signals repeatedly across networks. Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search. The rules determine the impact of each node in a pathway, which is used to score the probability of the pathway's modulation by chance. We have comprehensively tested BONITA for application to transcriptomics data from translational studies. Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivity at lower levels of source node modulation and similar sensitivity at higher levels of source node modulation. Application of BONITA pathway analysis to previously validated RNA-sequencing studies identifies additional relevant pathways in in-vitro human cell line experiments and in-vivo infant studies. Additionally, BONITA successfully detected modulation of disease specific pathways when comparing relevant RNA-sequencing data with healthy controls. Most interestingly, the two highest impact score nodes identified by BONITA included known drug targets. Thus, BONITA is a powerful approach to prioritize not only pathways but also specific mechanistic role of genes compared to existing methods. BONITA is available at: https://github.com/thakar-lab/BONITA.
通路分析被广泛应用于从高通量组学数据中获取机制见解。然而，大多数现有的方法并没有考虑到通路拓扑所代表的信号整合，从而导致下游基因受到调节时富集了收敛的通路。将信号流和整合纳入通路分析，可以根据关键调节基因的调节来对通路进行排序。由于参数化简单，离散状态网络建模可以为大规模数据提供便利的实现。在这里，我们使用离散状态动力学来模拟细胞异质性，并在横截面数据中测量通路活性。我们引入了一种新的算法，即布尔组学网络不变时间分析（BONITA），用于信号传播、信号整合和通路分析。我们的信号传播方法将转录组数据中的异质性建模为源于细胞间异质性而不是细胞内随机性，并且将二进制信号在网络中反复传播。逻辑规则定义信号整合是通过遗传算法推断出来的，并通过局部搜索进行细化。这些规则确定了通路中每个节点的影响，这用于根据机会来评分通路的调节概率。我们已经全面测试了 BONITA 在转化研究中的转录组学数据中的应用。与最先进的通路分析方法的比较表明，BONITA 在源节点调节较低水平时具有更高的灵敏度，而在源节点调节较高水平时具有相似的灵敏度。将 BONITA 通路分析应用于先前验证的 RNA-seq 研究，在体外人细胞系实验和体内婴儿研究中确定了其他相关通路。此外，当将相关的 RNA-seq 数据与健康对照进行比较时，BONITA 成功检测到了疾病特异性通路的调节。最有趣的是，BONITA 确定的两个影响得分最高的节点包括已知的药物靶点。因此，与现有的方法相比，BONITA 是一种强有力的方法，可以不仅优先考虑通路，还可以优先考虑基因的特定机制作用。BONITA 可在以下网址获得：https://github.com/thakar-lab/BONITA。
