Differential variability analysis of gene expression and its application to human diseases.
Differential variability analysis of gene expression and its application to human diseases.
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DOI：
10.1093/bioinformatics/btn142
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发表时间：
2008-07-01
期刊：
影响因子：
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通讯作者：
Charleston MA
中科院分区：
文献类型：
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作者：
Ho JW;Stefani M;dos Remedios CG;Charleston MA
动机：当前的微阵列分析侧重于识别在不同生物状态（例如患病与未患病）下差异表达（DE）或差异共表达（DC）的基因集。我们观察到，在许多人类疾病中，一些基因的表达变异性（方差）有显著的增加或减少。由于这些观察到的表达变异性变化可能是由潜在表达动态的改变引起的，所以这种差异变异性（DV）模式在生物学上也很有趣。
结果：在此，我们提出一种针对样本组间基因表达变异性变化的新型分析方法，我们称之为差异变异性分析。我们引入差异变异性（DV）的概念，并提出一种从微阵列数据中识别DV基因的简单流程。我们用模拟的和真实的微阵列数据集对我们的流程进行了评估。研究了数据预处理方法对DV基因识别的影响。用四个人类疾病数据集证明了DV分析的生物学意义。研究了DV、DE和DC基因之间的关系。结果表明，表达变异性的变化与共表达模式的变化相关，这意味着DV不仅仅是随机噪声，而是有信息的信号。
可用性：差异变异性分析的R源代码可应要求从通讯作者处获取。
联系人：joshua@it.usyd.edu.au；mcharleston@it.usyd.edu.au
Motivation: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significantincrease or decrease in expression variability (variance). Asthese observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting. Results: Here we propose a novel analysis for changes in gene expression variability between groups of amples, which we call differential variability analysis. We introduce the concept of differential variability (DV), and present a simple procedure for identifying DV genes from microarray data. Our procedure is evaluated with simulated and real microarray datasets. The effect of data preprocessing methods on identification of DV gene is investigated. The biological significance of DV analysis is demonstrated with four human disease datasets. The relationships among DV, DE and DC genes are investigated. The results suggest that changes in expression variability are associated with changes in coexpression pattern, which imply that DV is not merely stochastic noise, but informative signal. Availability: The R source code for differential variability analysis is available from the contact authors upon request. Contact: joshua@it.usyd.edu.au; mcharleston@it.usyd.edu.au