NDLI: Assessing reliability of gene clusters from gene expression data

Content Provider	Springer Nature Link
Author	Zhang, Kui Zhao, Hongyu
Copyright Year	2000
Abstract	The rapid development of microarray technologies has raised many challenging problems in experiment design and data analysis. Although many numerical algorithms have been successfully applied to analyze gene expression data, the effects of variations and uncertainties in measured gene expression levels across samples and experiments have been largely ignored in the literature. In this article, in the context of hierarchical clustering algorithms, we introduce a statistical resampling method to assess the reliability of gene clusters identified from any hierarchical clustering method. Using the clustering trees constructed from the resampled data, we can evaluate the confidence value for each node in the observed clustering tree. A majority-rule consensus tree can be obtained, showing clusters that only occur in a majority of the resampled trees. We illustrate our proposed methods with applications to two published data sets. Although the methods are discussed in the context of hierarchical clustering methods, they can be applied with other cluster-identification methods for gene expression data to assess the reliability of any gene cluster of interest.
Starting Page	156
Ending Page	173
Page Count	18
File Format	PDF
ISSN	1438793X
Journal	Functional & Integrative Genomics
Volume Number	1
Issue Number	3
Language	English
Publisher	Springer-Verlag
Publisher Date	2014-02-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cell Biology Plant Genetics & Genomics Microbial Genetics and Genomics Biochemistry Bioinformatics Animal Genetics and Genomics
Content Type	Text
Resource Type	Article
Subject	Genetics Medicine

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