Assessing reliability of gene clusters from gene expression data |
| |
Authors: | Kui Zhang Hongyu Zhao |
| |
Institution: | (1) Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA, |
| |
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.
Electronic Publication |
| |
Keywords: | Gene expression Hierarchical clustering Bootstrap Consensus tree |
本文献已被 SpringerLink 等数据库收录! |
|