A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments
A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments
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DOI：
10.1093/bioinformatics/btm292
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发表时间：
2007-08-15
期刊：
影响因子：
5.8
通讯作者：
Permutt, M. Alan.
中科院分区：
文献类型：
--
作者：
Zhou, Yiyong;Cras-Meneur, Corentin;Permutt, M. Alan.
动机：目前大多数鉴定差异表达基因的方法属于所谓的单基因分析，即在逐个基因的基础上进行假设检验。在单基因分析方法中，需要估计每个基因的变异性以确定基因是否差异表达。变异性估计的准确性差，很难识别的基因与小的倍数变化，除非一个非常大的数量的重复experiments.Results：我们提出了一种方法，可以避免困难的任务，估计每个基因的变异性，同时可靠地识别一组差异表达的基因与低的错误发现率，即使当倍数变化是非常小的。在这篇文章中，一个新的表征差异表达基因的基础上建立了一个定理的分布排列的基因（对数）的比例在每个阵列内。这种基于等级的差异表达基因的表征是全基因分析而不是单基因分析的一个例子。我们应用该方法的cDNA微阵列数据集和许多低倍数变化的基因（低至1.3倍的变化）可靠地确定，而无需进行假设检验的基因的基础上。错误发现率以两种不同的方式估计，反映了所有基因的变异性，而没有与多重假设检验相关的并发症。我们还提供了我们的方法和基于单基因分析的方法之间的一些比较。联系方式：yyzhou@netra.wustl. edu补充信息：补充数据可在生物信息学在线获得。
Motivation: Currently most of the methods for identifying differentially expressed genes fall into the category of so called single-geneanalysis, performing hypothesis testing on a gene-by-gene basis. In a single-gene-analysis approach, estimating the variability of each gene is required to determine whether a gene is differentially expressed or not. Poor accuracy of variability estimation makes it difficult to identify genes with small fold-changes unless a very large number of replicate experiments are performed.Results: We propose a method that can avoid the difficult task of estimating variability for each gene, while reliably identifying a group of differentially expressed genes with low false discovery rates, even when the fold-changes are very small. In this article, a new characterization of differentially expressed genes is established based on a theorem about the distribution of ranks of genes sorted by ( log) ratios within each array. This characterization of differentially expressed genes based on rank is an example of all-gene-analysis instead of single gene analysis. We apply the method to a cDNA microarray dataset and many low fold-changed genes ( as low as 1.3 fold-changes) are reliably identified without carrying out hypothesis testing on a gene-by-gene basis. The false discovery rate is estimated in two different ways reflecting the variability from all the genes without the complications related to multiple hypothesis testing. We also provide some comparisons between our approach and single-gene-analysis based methods.Contact: yyzhou@netra.wustl.eduSupplementary information: Supplementary data are available at Bioinformatics online.