Department of Statistics, Seoul National University, Seoul 08826, Korea.
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac171.
Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.
许多用于通路分析的统计方法已经被用于识别与疾病相关的通路以及基因和蛋白质等生物因素。然而，大多数通路分析方法忽略了生物因素与通路之间复杂的非线性关系。在这项研究中，我们提出了一种使用层次结构组件模型的深度学习通路分析方法（DeepHisCoM），该方法利用深度学习通过构建一个考虑层次生物结构的多层模型来考虑生物因素对通路的非线性复杂贡献。通过模拟研究，与传统通路方法相比，DeepHisCoM 在非线性通路效应中具有更高的功效，并且在线性通路效应中具有可比的功效。将 DeepHisCoM 应用于肝细胞癌（HCC）组学数据集，包括代谢组学、转录组学和宏基因组学数据集，表明它成功地识别了三个与 HCC 高度相关的已知通路，如赖氨酸降解、缬氨酸、亮氨酸和异亮氨酸生物合成以及苯丙氨酸、酪氨酸和色氨酸。将 DeepHisCoM 应用于 2019 年冠状病毒病（COVID-19）单核苷酸多态性（SNP）数据集也表明，它识别了四个与 COVID-19 严重程度高度相关的通路，如丝裂原激活蛋白激酶（MAPK）信号通路、促性腺激素释放激素（GnRH）信号通路、肥厚型心肌病和扩张型心肌病。代码可在 https://github.com/chanwoo-park-official/DeepHisCoM 上获得。
