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Gene set analysis using sufficient dimension reduction

机译:使用足够的维数减少进行基因集分析

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摘要

BackgroundGene set analysis (GSA) aims to evaluate the association between the expression of biological pathways, or a priori defined gene sets, and a particular phenotype. Numerous GSA methods have been proposed to assess the enrichment of sets of genes. However, most methods are developed with respect to a specific alternative scenario, such as a differential mean pattern or a differential coexpression. Moreover, a very limited number of methods can handle either binary, categorical, or continuous phenotypes. In this paper, we develop two novel GSA tests, called SDRs, based on the sufficient dimension reduction technique, which aims to capture sufficient information about the relationship between genes and the phenotype. The advantages of our proposed methods are that they allow for categorical and continuous phenotypes, and they are also able to identify a variety of enriched gene sets.
机译:背景基因集分析(GSA)旨在评估生物途径表达或先验定义的基因集与特定表型之间的关联。已经提出了许多GSA方法来评估基因集的富集。但是,大多数方法是针对特定的替代方案开发的,例如差分平均模式或差分共表达。此外,数量非常有限的方法可以处理二元,分类或连续表型。在本文中,我们基于充分的降维技术开发了两种新颖的GSA测试,称为SDR,旨在捕获关于基因与表型之间关系的足够信息。我们提出的方法的优点是它们允许分类和连续的表型,并且它们还能够识别多种富集的基因集。

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