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Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments (2002)
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| Venue: | STATISTICA SINICA |
| Citations: | 436 - 12 self |
BibTeX
@ARTICLE{Dudoit02statisticalmethods,
author = {Sandrine Dudoit and Yee Hwa Yang and Matthew J. Callow and Terence P. Speed},
title = {Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments},
journal = {STATISTICA SINICA},
year = {2002},
volume = {12},
pages = {111--139}
}
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Abstract
DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. Although it is not the main focus of the paper, new methods for the important pre-processing steps of image analysis and normalization are proposed. Given suitably normalized data, the biological question of differential expression is restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and responses or covariates of interest. Di erentially expressed genes are identified based on adjusted p-values for a multiple testing procedure which strongly controls the family-wise Type I error rate and takes into account the dependence structure between the gene expression levels. No specific parametric form is assumed for the distribution of the test statistics and a permutation procedure is used to estimate adjusted p-values. Several data displays are suggested for the visual identification of differentially expressed genes and of important features of these genes. The above methods are applied to microarray data from a study of gene expression in the livers of mice with very low HDL cholesterol levels. The genes identified using data from multiple slides are compared to those identified by recently published single-slide methods.
Keyphrases
replicated cdna microarray experiment statistical method expression level new method promising biotechnology dna microarrays test statistic permutation procedure several data display multiple slide simultaneous test biological question family-wise type gene expression level important pre-processing step gene expression multiple testing procedure visual identification single-slide method null hypothesis adjusted p-values important feature main focus image analysis specific parametric form dependence structure multiple hypothesis testing present paper differential expression low hdl cholesterol level
