Motivation

Genome-wide multi-omics profiling of complex diseases provides valuable resources and opportunities to discover associations between various measures of genes and diseases. Currently, a pressing challenge is how to effectively detect functional genes associated with or causing phenotypic outcomes. We developed CNet to identify groups of genomic signatures whose combinatory effect is significantly associated with clinical and phenotypical outcomes.

Results

CNet builds on a generalized sequential feedforward method, augmented by a down-sampling bootstrap strategy to reduce random hitchhiking signatures. It further applies a dynamic trimming procedure to remove relatively less informative signatures at every step. CNet can manage heterogeneous genomic signature profiles simultaneously and select the best signature to represent a specific gene. To deal with various forms of clinical and phenotypical measurements, we introduced four models to deal with continuous, categorical and censored data. We tested CNet using drug-response data, multidimensional cancer genomics data and genome-wide association study data for multiple traits. Our results demonstrated that in various scenarios, CNet could effectively identify signatures that are associated with the outcomes. In addition, we applied CNet to identify likely disease-causing chains involving somatic mutations, pathway activities and patient outcomes. With appropriate setting, CNet can be applied in many biological conditions.

Availability and implementation

CNet can be downloaded at https://github.com/bsml320/CNet.

Supplementary information

Supplementary dataSupplementary data are available at Bioinformatics online.

中文翻译：

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CNet：一种多组学方法，用于检测临床相关的组合基因组特征

动机

复杂疾病的全基因组多组学分析提供了宝贵的资源和机会，可发现各种基因和疾病措施之间的关联。当前，紧迫的挑战是如何有效地检测与表型结果相关或引起表型结果的功能基因。我们开发了CNet，以鉴定组合效应与临床和表型结果显着相关的基因组标记组。

结果

CNet建立在通用顺序前馈方法的基础上，并通过下采样引导策略进行了增强，以减少随机搭便车信号。它还进一步应用了动态修整程序，以在每个步骤中删除相对较少的信息签名。CNet可以同时管理异质基因组特征谱，并选择最佳特征谱来代表特定基因。为了处理各种形式的临床和表型测量，我们引入了四个模型来处理连续，分类和审查的数据。我们使用药物反应数据，多维癌症基因组学数据和全基因组关联研究数据针对多种性状测试了CNet。我们的结果表明，在各种情况下，CNet都可以有效地识别与结果相关的签名。此外，我们应用CNet来识别涉及体细胞突变，途径活动和患者预后的可能的致病链。通过适当的设置，CNet可以应用于许多生物学条件。

可用性和实施

可以从https://github.com/bsml320/CNet下载CNet。

补充资料

补充数据补充数据可从Bioinformatics在线获得。