Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, Republic of Korea.
School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Deagu, Republic of Korea.
PLoS One. 2023 Mar 16;18(3):e0278272. doi: 10.1371/journal.pone.0278272. eCollection 2023.
Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.
通路由形成网络的蛋白质组成，用于表示特定的生物学机制，通常用于基于基因列表测量富集分数，以衡量其生物学活性。通路分析是大多数基因组和转录组研究中事实上的下游分析程序。在这里，我们提出了 MOPA（多组学通路分析），这是一种多组学整合方法，以个体通路为单位，以富集的多组学调控活性为基础对样本进行评分，我们称之为 mES（多组学富集评分）。mES 评分反映了多组学之间调控关系的强度，单位为通路。此外，MOPA 能够衡量每个组学对 mES 的贡献程度，可用于观察在样本组内（例如，亚型、性别）哪种组学在通路中活跃，我们称之为 OCR（组学贡献率）。使用 9 种不同的癌症类型、93 个临床特征和 3 种组学（即基因表达、miRNA 和甲基化），MOPA 用于搜索可从多组学角度解释的临床特征。通过评估 MOPA 的性能，我们表明它与之前的单一组学和多组学通路分析工具相比，具有更高或至少相等的性能。我们发现，MOPA 的优势在于能够使用 mES 和 OCR 从组学关系的角度解释通路。作为结果之一，TGF-β 信号通路被捕获为一个重要的通路，该通路在结肠腺癌的 CMS4 亚型中表现出独特的 mES 和 OCR 值。mES 和 OCR 指标表明，mRNA 和 miRNA 表达与其他亚型明显不同，这与之前的研究一致。MOPA 软件可在 https://github.com/jaeminjj/MOPA 上获取。
