RoDiCE: robust differential protein co-expression analysis for cancer complexome
RoDiCE: robust differential protein co-expression analysis for cancer complexome
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DOI：
10.1093/bioinformatics/btab612
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发表时间：
2021-09-16
期刊：
影响因子：
5.8
通讯作者：
Miyano, Satoru
中科院分区：
文献类型：
--
作者：
Matsui, Yusuke;Abe, Yuichi;Miyano, Satoru
动机：癌症相关蛋白复合体的全谱异常在很大程度上仍不清楚。比较肿瘤细胞和健康细胞之间每个蛋白质复合体的共表达结构可能会提供关于癌症特异性蛋白质功能障碍的见解。然而，基于质谱学的蛋白质组学的技术局限性，包括生物蛋白质变体的污染，会导致不可忽略的高估(或低估)共表达的噪声。结果：我们提出了一种基于差异蛋白质共表达测试的稳健的癌症蛋白质复合体异常识别算法。与传统的基于线性相关的方法相比，基于Copula的方法足以提高噪声数据下的识别精度。作为应用，我们使用来自肾癌的大规模蛋白质组数据来显示重要的蛋白质复合体、调节信号通路和药物靶点可以被识别。提出的方法超越了传统的线性相关性，提供了对高阶差异共表达结构的洞察。
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.