Yang Le, Chen Runpu, Goodison Steve, Sun Yijun
Department of Computer Science and Engineering, The State University of New York at Buffalo, Buffalo, NY, USA.
Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA.
Nat Comput Sci. 2021 Jan;1(1):79-88. doi: 10.1038/s43588-020-00009-4. Epub 2021 Jan 14.
The identification of key functional biological networks from high-dimensional genomics data is pivotal for cancer research. Here, we introduce FDRnet, a method for the detection of molecular subnetworks in cancer, which addresses several challenges in pathway analysis. FDRnet detects key subnetworks by solving a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint, and minimizing a conductance score to find dense subgraphs around seed genes. A large-scale benchmark study was performed on both simulation and cancer genomics data. FDRnet outperformed other methods in the ability to detect functionally homogeneous subnetworks in a scale-free biological network, to control FDRs of the genes in detected subnetworks, to improve computational efficiency and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases.
从高维基因组数据中识别关键的功能性生物网络对于癌症研究至关重要。在此，我们介绍FDRnet，一种用于检测癌症中分子子网的方法，该方法解决了通路分析中的几个挑战。FDRnet通过解决一个混合整数线性规划问题来检测关键子网，使用给定的错误发现率（FDR）上限作为预算约束，并最小化一个电导分数以找到种子基因周围的密集子图。我们对模拟数据和癌症基因组数据都进行了大规模的基准研究。FDRnet在检测无标度生物网络中功能同质子网的能力、控制检测到的子网中基因的FDR、提高计算效率以及整合多组学数据方面优于其他方法。通过克服现有方法的局限性，FDRnet可以促进癌症和其他遗传疾病中关键功能通路的检测。
