In the era of big data, integrative analyses that pool data from different sources are now extensively conducted in order to improve performance. Among many interesting applications, genomics research is an area where integrative methods become popular tools to identify prognostic biomarkers for various diseases. In this paper, we propose such a framework for pathway and gene identification. Our method employs a hierarchical decomposition on genes' effects followed by a proper regularization to identify important pathways and genes across multiple studies. Asymptotic theories are provided to show that our method is both pathway and gene selection consistent. More importantly, we explicitly show that pathway selection consistency needs milder statistical conditions than gene selection consistency, as it would allow false positives and negatives at the gene selection level. Finite-sample performance of our method is shown to be superior than other ad hoc methods in various simulation studies. We further apply our method to analyze five cardiovascular disease studies. Our method is intrinsically a general method on group-wise and element-wise selections from integrative analysis, which can have other applications beyond genomic research.

中文翻译：

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综合分析中通路和基因鉴定的统计框架

在大数据时代，为了提高性能，现在广泛进行了汇集来自不同来源的数据的综合分析。在许多有趣的应用中，基因组学研究是一个综合方法成为识别各种疾病预后生物标志物的流行工具的领域。在本文中，我们提出了这样一个用于通路和基因鉴定的框架。我们的方法对基因的影响采用分层分解，然后进行适当的正则化，以识别多项研究中的重要途径和基因。提供了渐近理论来表明我们的方法在途径和基因选择上都是一致的。更重要的是，我们明确表明通路选择一致性需要比基因选择一致性更温和的统计条件，因为它会允许在基因选择级别出现假阳性和阴性。在各种模拟研究中，我们方法的有限样本性能被证明优于其他临时方法。我们进一步应用我们的方法来分析五项心血管疾病研究。我们的方法本质上是一种从综合分析中进行分组和元素选择的通用方法，除了基因组研究之外，它还可以有其他应用。