完全捕获细胞状态需要检查生物样本的基因组、表观基因组、转录组、蛋白质组和其他分析，并需要综合计算模型来推理复杂且有时相互矛盾的测量结果。对这些所谓的多组学数据进行建模在疾病分析中特别有益，其中跨组学数据类型的观察可能会揭示意想不到的患者分组并为临床结果和治疗提供信息。我们提出了癌症多组学通路分析（MPAC），这是一种计算框架，可通过生物通路的先验知识解释多组学数据。 MPAC 使用因子图在通路中编码的网络关系，从多组学数据推断蛋白质和相关通路实体的一致活性水平，运行排列测试以消除虚假的活性预测，并按通路活动对生物样本进行分组，以优先考虑具有潜在临床价值的蛋白质关联。以《癌症基因组图谱》中头颈鳞状细胞癌患者的 DNA 拷贝数改变和 RNA-seq 数据为例，我们证明 MPAC 预测与免疫反应相关的患者亚组，而这些免疫反应并未通过单独输入组学数据类型的分析来识别。通过该亚组鉴定的关键蛋白质具有与临床结果以及免疫细胞组成相关的途径活性。我们的 MPAC R 软件包可在 https://bioconductor.org/packages/MPAC 上获取，可对新数据集进行类似的多组学分析。

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cancer (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC uses network relationships encoded in pathways using a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to prioritize proteins with potential clinical relevance. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC , enables similar multi-omic analyses on new datasets.