A Mixture Model Approach for the Analysis of Microarray Gene Expression Data
Abstract
Microarrays have emerged as powerful tools allowing investigators to assess the expression of thousands of genes in different tissues and organisms. Statistical treatment of the resulting data remains a substantial challenge. Investigators using microarray expression studies may wish to answer questions about the statistical significance of differences in expression of any of the genes under study, avoiding false positive and false negative results. We have developed a sequence of procedures involving finite mixture modeling and bootstrap inference to address these issues in studies involving many thousands of genes. We illustrate the use of these techniques with a dataset involving calorically restricted mice. © 2002 Elsevier Science B.V. All rights reserved.
Recommended Citation
D. B. Allison et al., "A Mixture Model Approach for the Analysis of Microarray Gene Expression Data," Computational Statistics and Data Analysis, vol. 38, no. 5, pp. 1 - 20, Elsevier, Mar 2002.
The definitive version is available at https://doi.org/10.1016/s0167-9473(01)00046-9
Department(s)
Mathematics and Statistics
International Standard Serial Number (ISSN)
0167-9473
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
28 Mar 2002
Comments
National Institutes of Health, Grant 5 U24 DK58776