Wisdom of Crowds: Integrating Gene Set Enrichment Studies Involving RNA-Seq.

 产品说明：基因集富集分析（GSEA）的目的是确定重要的途径，或更一般地说，涉及复杂的人类疾病的生物学相关基因集。由于它提供的许多优点，GSEA已被证明是至关重要的系统生物学研究，可以导致疾病发病机制的基本生物过程的综合理解，以及定义治疗靶点的元素，以及对治疗选择的反应。然而，尽管它在促进人类健康的潜在重要性，它是惊人的，从孤立的研究得出的结论GSEA往往是稀疏的，不同的研究可能会导致不一致的，有时是矛盾的结果。这个问题主要与以下限制有关。首先，研究表明，异构体特异性表达变异在复杂的人类疾病中发挥重要作用。然而，传统上用于mRNA分析的微阵列技术通常缺乏测量异构体特异性表达所需的分辨率。其次，个体全基因组转录组学研究的样本量相对于压倒性数量的基因通常是不够的。 在下一代测序（NGS）技术之后，已经可以测量全基因组同种型特异性表达水平，这要求可以利用非优先分辨率的下一代创新。此外，从各种微阵列和RNA-seq实验中已经创建了大量的数据;并且数量继续快速增长。所有这些都引起了对开发整合GSEA（iGSEA）方法的巨大需求，所述方法允许明确利用同种型特异性表达，以联合收割机组合多个相关研究，以避免从个体数据得出不确定或潜在的指导性结论，从而增强分析的能力、再现性和可解释性。 该项目的目标是为iGSEA开发新的统计方法和生物信息学工具，以有效地从涉及新兴RNA-Seq实验以及传统微阵列实验的研究中合成多样化的mRNA表达数据，重点是整合异构体特异性表达。在目标1中，我们将开发一种使用亚型特异性表达的iGSEA的创新荟萃分析方法。具体来说，我们将把新提出的用于全基因组关联研究荟萃分析的exe-effect和random-effects模型的想法纳入iGSEA，以实现最大可能的统计效率，同时允许纳入异质性研究。目标2将提出稳健的荟萃分析方法，以整合来自各种来源的亚型和基因水平的表达数据。目标3将开发一种完全集成的贝叶斯方法，以更有效地整合现有的生物信息。一个强大的贝叶斯分层方法将被提出来联合建模不同的信息源。这不仅将大大提高iGSEA的能力，而且还能同时揭示有趣的基因和基因集，以及每个已识别基因的“负责任”亚型。

 DESCRIPTION: Gene Set Enrichment Analysis (GSEA) aims at identifying essential pathways, or more generally, sets of biologically related genes that are involved in complex human diseases. Due to many advantages it offers, GSEA has been proved to be crucial in systems biology studies that can lead to an integrated understanding of fundamental biological processes underlying disease pathogenesis, and elements defining therapeutic targets as well as responses to treatment selections. However, despite its potential importance in promoting human health, it is striking that conclusions of GSEA drawn from isolated studies are often sparse, and different studies may lead to inconsistent and sometimes contradictory results. This problem is largely related to the following limitations. Firstly, studies have shown that isoform-specific expression variations play important roles in complex human diseases. However, the microarray technology traditionally used for mRNA profiling often lacks the resolution needed to measure isoform-specific expression. Secondly, sample sizes of individual genome-wide transcriptomic studies are typically insufficient relative to an overwhelming number of genes. In the wake of next generation sequencing (NGS) technologies, it has been made possible to measure genome-wide isoform-specific expression levels, calling for next generation innovations that can utilize the un- precedence resolution. Further, enormous amounts of data have been created from various microarray and RNA-seq experiments; and the volume continues to grow fast. All these give rise to tremendous demand for developing methods of integrative GSEA (iGSEA) that allow for explicit utilization of isoform-specific expression, to combine multiple relevant studies, in order to avoid indecisive or potentially conducting conclusions from individual data and so to enhance the power, reproducibility and interpretability of the analysis. The goal of this project is to develop novel statistical methods and bioinformatical tools for iGSEA to efficiently synthesize diverse mRNA expression data from studies involving newly emerging RNA-Seq experiments as well as conventional microarray experiments, with an emphasis on integrating isoform-specific expression. In Aim 1, we will develop an innovative meta-analysis method for iGSEA using isoform-specific expression. Specifically, we will incorporate ideas from exe-effect and random-effects models, newly proposed and tested for meta-analysis of genome-wide association studies, into iGSEA, in order to achieve the maximum possible statistical efficiency while allowing for inclusion of heterogeneous studies. Aim 2 will propose robust meta-analysis methods to integrate both isoform- and gene-level expression data from a variety of sources. Aim 3 will develop a fully integrated Bayesian method to incorporate existing biological information more effectively. A powerful Bayesian hierarchical approach will be proposed to jointly model different sources of information. This will not only drastically improve the power of iGSEA, but also simultaneously reveal interesting genes and gene sets, as well as `responsible' isoforms of each identified gene.