A statistical perspective on gene expression data analysis Book Section
| Authors: | Satagopan, J. M.; Panageas, K. S. |
| Editor: | D'Agostino, R. B. |
| Article/Chapter Title: | A statistical perspective on gene expression data analysis |
| Abstract: | Rapid advances in biotechnology have resulted in an increasing interest in the use of oligonucleotide and spotted cDNA gene expression microarrays for medical research. These arrays are being widely used to understand the underlying genetic structure of various diseases, with the ultimate goal to provide better diagnosis, prevention and cure. This technology allows for measurement of expression levels from several thousands of genes simultaneously, thus resulting in an enormous amount of data. The role of the statistician is critical to the successful design of gene expression studies, and the analysis and interpretation of the resulting voluminous data. This paper discusses hypotheses common to gene expression studies, and describes some of the statistical methods suitable for addressing these hypotheses. S-plus and SAS codes to perform the statistical methods are provided. Gene expression data from an unpublished oncologic study is used to illustrate these methods. © 2004 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester. All Rights Reserved. |
| Keywords: | classification; class prediction; class discovery; compound covariate predictor; fisher's linear discriminant function; multiple correction; hierarchical clustering |
| Book Title: | Tutorials in Biostatistics: Statistical Modelling of Complex Medical Data |
| Volume: | 2 |
| ISBN: | 978-0-470-02370-9 |
| Publisher: | Wiley Blackwell |
| Publication Place: | West Sussex, UK |
| Date Published: | 2004-01-01 |
| Start Page: | 361 |
| End Page: | 379 |
| Language: | English |
| DOI: | 10.1002/0470023724.ch1d(vi) |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Book Chapter -- Export Date: 3 March 2016 -- Source: Scopus |
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