Motivation The full spectrum of abnormalities in cancer-associated protein complexes remains largely unknown. Comparing the co-expression structure of each protein complex between tumor and healthy cells may provide insights regarding cancer-specific protein dysfunction. However, the technical limitations of mass spectrometry-based proteomics, including contamination with biological protein variants, causes noise that leads to non-negligible over- (or under-) estimating co-expression. Results We propose a robust algorithm for identifying protein complex aberrations in cancer based on differential protein co-expression testing. Our method based on a copula is sufficient for improving identification accuracy with noisy data compared to conventional linear correlation-based approaches. As an application, we use large-scale proteomic data from renal cancer to show that important protein complexes, regulatory signaling pathways and drug targets can be identified. The proposed approach surpasses traditional linear correlations to provide insights into higher-order differential co-expression structures. Availability and implementation https://github.com/ymatts/RoDiCE. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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RoDiCE：癌症复合体的稳健差异蛋白共表达分析

动机 癌症相关蛋白复合物的全谱异常在很大程度上仍然未知。比较肿瘤和健康细胞之间每种蛋白质复合物的共表达结构可能会提供有关癌症特异性蛋白质功能障碍的见解。然而，基于质谱的蛋白质组学的技术局限性，包括生物蛋白质变体的污染，会导致噪声导致不可忽略的过度（或不足）估计共表达。结果 我们提出了一种基于差异蛋白共表达测试来识别癌症中蛋白质复合物畸变的稳健算法。与传统的基于线性相关的方法相比，我们基于 copula 的方法足以提高噪声数据的识别精度。作为一个应用程序，我们使用来自肾癌的大规模蛋白质组学数据来表明可以识别重要的蛋白质复合物、调节信号通路和药物靶标。所提出的方法超越了传统的线性相关性，以提供对高阶微分共表达结构的洞察力。可用性和实施​​ https://github.com/ymatts/RoDiCE。补充信息 补充数据可在 Bioinformatics 在线获取。