Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications. A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT). The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

中文翻译：

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增强了对基因表达重要调控子的识别。

诸如癌症之类的疾病将导致基因表达的变化，因此确定与这些变化直接相关的关键调控基因至关重要。这可以通过计算相关监管机构的监管影响因子（RIF）得分来完成。但是，此计算是基于估计基因表达的相关模式（通常是皮尔逊相关性）以及对一组特定调节剂（通常是转录因子）的假设。这项研究使用Fisher和Sobolev指标以及一组扩展的调控子（包括表观遗传调控子和长的非编码RNA（lncRNA））探索了相关的替代度量。关于前列腺癌的数据已用于探索这些修饰的作用。开发了一种用于RIF分数计算的工具，该工具具有其他相关度量和一组扩展的调节剂，并针对前列腺癌的基因表达数据进行了测试。这项研究表明，Fisher和Sobolev度量标准可以改善对前列腺癌中基因表达的有据可查的调节剂的鉴定，并且已鉴定的关键调节剂的集合与先前定义的与癌症相关的基因集表现出更好的重叠。扩展的调节子集可识别出一些值得进一步研究的有趣候选物，包括lncRNA。几个关键过程被认为是重要的，包括纺锤体组装和上皮-间质转化（EMT）。研究表明，基于基因组数据中基因表达的相关性，使用替代性相关性度量可以改善工具的性能。在其他基于相关性的应用程序中也应考虑Fisher和Sobolev指标。