Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples.
Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples.
复制标题
综合富集分析：一种检测异质样本中失调途径的新计算方法
DOI：
10.1186/s12864-015-2188-7
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发表时间：
2015-11-10
期刊：
影响因子：
4.4
通讯作者：
Li G
中科院分区：
文献类型：
--
作者：
Yu X;Zeng T;Li G
途径富集分析是研究生物学和生物医学的一个有用的工具，因为它对明确的生物程序而不是单独的分子进行功能筛选。当对通路进行富集分析时，测量具有表型变化的通路的故障，例如从正常到患病，是关键问题。差异表达基因（differential expression genes, DEGs）在传统分析中受到广泛关注，这是基于样品的高纯度。然而，疾病样本通常是异质的，因此，具有巨大差异表达方差（devg）的基因在指示生物系统的特定状态方面变得越来越有吸引力和重要。在差异表达变异的背景下，测量一个通路的富集或状态仍然是一个挑战。为了解决这一问题，我们提出了基于一种新的富集测量方法的综合富集分析（IEA）。结果IEA的主要竞争能力是同时识别含有deg和devg的失调通路，而这一点通常被其他方法所强调。接下来，IEA提供了两种额外的辅助方法来研究这种失调的途径。一是通过估计通路串扰来推断已识别的失调通路与预期目标通路之间的关联。另一个是根据devg的相对表达而不是传统的原始表达来识别亚型因子作为与特定临床指标相关的失调途径。基于先前建立的评估方案，我们发现，在特定的队列（即一组来自人类患者的真实基因表达数据集）中，IEA可以显著提高一些目标疾病通路的排名，这比其他最先进的方法更有效。此外，我们提出了一项关于糖尿病的概念验证研究，表明：IEA而不是传统的ORA或GSEA可以捕获充满devg和deg的被低估的失调通路；这些新发现的途径可以通过估计的串串与先前已知的疾病途径显著相关；IEA识别的许多候选亚型因子也与基因型-表型关联亚型的风险有显著关系。结论总的来说，IEA为在复杂的临床应用背景下（即疾病的异质性）进行富集分析提供了一种新的工具，是对常规方法的必要补充和合作。
BackgroundPathway enrichment analysis is a useful tool to study biology and biomedicine, due to its functional screening on well-defined biological procedures rather than separate molecules. The measurement of malfunctions of pathways with a phenotype change, e.g., from normal to diseased, is the key issue when applying enrichment analysis on a pathway. The differentially expressed genes (DEGs) are widely focused in conventional analysis, which is based on the great purity of samples. However, the disease samples are usually heterogeneous, so that, the genes with great differential expression variance (DEVGs) are becoming attractive and important to indicate the specific state of a biological system. In the context of differential expression variance, it is still a challenge to measure the enrichment or status of a pathway. To address this issue, we proposed Integrative Enrichment Analysis (IEA) based on a novel enrichment measurement.ResultsThe main competitive ability of IEA is to identify dysregulated pathways containing DEGs and DEVGs simultaneously, which are usually under-scored by other methods. Next, IEA provides two additional assistant approaches to investigate such dysregulated pathways. One is to infer the association among identified dysregulated pathways and expected target pathways by estimating pathway crosstalks. The other one is to recognize subtype-factors as dysregulated pathways associated to particular clinical indices according to the DEVGs’ relative expressions rather than conventional raw expressions. Based on a previously established evaluation scheme, we found that, in particular cohorts (i.e., a group of real gene expression datasets from human patients), a few target disease pathways can be significantly high-ranked by IEA, which is more effective than other state-of-the-art methods. Furthermore, we present a proof-of-concept study on Diabetes to indicate: IEA rather than conventional ORA or GSEA can capture the under-estimated dysregulated pathways full of DEVGs and DEGs; these newly identified pathways could be significantly linked to prior-known disease pathways by estimated crosstalks; and many candidate subtype-factors recognized by IEA also have significant relation with the risk of subtypes of genotype-phenotype associations.ConclusionsTotally, IEA supplies a new tool to carry on enrichment analysis in the complicate context of clinical application (i.e., heterogeneity of disease), as a necessary complementary and cooperative approach to conventional ones.