Utility of Correlation Measures in Analysis of Gene Expression

被引：22

作者：

Almudevar A. ^{[1
]}

Klebanov L.B. ^{[2
]}

Qiu X. ^{[1
]}

Salzman P. ^{[1
]}

Yakovlev A.Y. ^{[1
]}

机构：

[1] Department of Biostatistics and Computational Biology, University of Rochester, Rochester

[2] Department of Probability and Statistics, Charles University, Institute of Informatics and Control, CZ-18675, Sokolovska 83

来源：

NeuroRX | 2006年 / 3卷 / 3期

基金：

美国国家卫生研究院;

关键词：

correlation; Gene expression; genetic networks; microarrays;

D O I：

10.1016/j.nurx.2006.05.037

中图分类号：

学科分类号：

摘要：

Summary: The role of the correlation structure of gene expression data are two-fold: It is a source of complications and useful information at the same time. Ignoring the strong stochastic dependence between gene expression levels in statistical methodologies for microarray data analysis may deteriorate their performance. However, there is a host of valuable information in the correlation structure that deserves a closer look. A proper use of correlation measures can remedy deficiencies of currently practiced methods that are focused too heavily on strong effects in terms of differential expression of genes. The present paper discusses the utility of correlation measures in microarray data analysis and gene regulatory network reconstruction, along with various pitfalls in both research areas that have been uncovered in methodological studies. These issues have broad applicability to all genomic studies examining the biology, diagnosis, and treatment of neurological disorders. © 2006 The American Society for Experimental NeuroTherapeutics, Inc.

引用

页码：384 / 395

页数：11

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