一种基于基因表达变异的途径富集分析方法

Pub Date : 2013-01-01 DOI:10.3724/sp.j.1206.2012.00410
Jia Xiao
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引用次数: 0

摘要

目前的途径富集方法主要是基于差异表达的基因,没有一种富集方法考虑途径的可变性(方差)。我们观察到,在疾病的表型中,一些途径在描述适当统计的变异性方面显着增加或减少。因此,在本文中,我们假设单一通路的变异在两种表型之间存在显著差异。设计了14种统计量及其检验方法,分析两种表型间通路变异和通路富集显著性,并与文献检索结果进行比较。同时,研究了五种不同的数据预处理方法对数据的处理效果。结果表明,RMA在5种基因表达数据预处理方法中都是稳定的。两种表型之间的通路变异是不同的。根据文献研究结果,排列检验结合每个基因的欧几里得距离方差(第11种方法)比GSEA更能有效地识别出显著的通路。综上所述,基于途径变异的途径富集分析策略是可行的,可为富集分析提供理论指导,为人类疾病研究提供新的生物学见解。
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A Method of Pathway Enrichment Analysis Based Gene Expression Variability
Current pathway enrichment method is mainly based on the gene that are differentially expressed,and no enrichment method considers pathway variability(variance).We observed that in the phenotype of disease,some pathways have a significant increase or decrease in variability describing appropriate statistics.Therefore,in this article,we hypothesize that the variation of single pathway is significantly different between two phenotypes.We designed fourteen types of statistics coupled with their test methods to analyze pathways variation and the pathways enrichment significance between two phenotypes,and we compared the results with those obtained by document retrieval.At the same time,the results of five different data preprocessing methods on data were investigated.The results show that RMA is stable in the five gene expression data preprocessing methods.The pathway variation is different between the two phenotypes.According to the literature research results,the permutation test coupled with the variance of Euclidean distance of each gene(the eleventh method) can identify significant pathways more efficiently than GSEA.In conclusion,pathway enrichment analysis strategy based on the pathway variation is feasible,which could be a theoretical guideline for enrichment analysis and a new biological insights of study in human diseases.
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