A kernel method used for the analysis of replicated micro-array experiments

Gannoun, Ali, Liquet, Beno, Saracco, Jerome and Urfer, Wolfgang (2007). A kernel method used for the analysis of replicated micro-array experiments. In Statistical methods for biostatistics and related fields (pp. 45-61) Berlin, Germany: Springer. doi:10.1007/978-3-540-32691-5_3


Author Gannoun, Ali
Liquet, Beno
Saracco, Jerome
Urfer, Wolfgang
Title of chapter A kernel method used for the analysis of replicated micro-array experiments
Title of book Statistical methods for biostatistics and related fields
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2007
Sub-type Chapter in textbook
DOI 10.1007/978-3-540-32691-5_3
Open Access Status
ISBN 9783540326908
9783540326915
Chapter number 3
Start page 45
End page 61
Total pages 17
Total chapters 19
Language eng
Abstract/Summary Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels of thousands of genes simultaneously. In microarray data analysis, the comparison of gene expression profiles with respect to different conditions and the selection of biologically interesting genes are crucial tasks. Multivariate statistical methods have been applied to analyze these large data sets. To identify genes with altered expression under two experimental conditions, we describe in this chapter a new nonparametric statistical approach. Specifically, we propose estimating the distributions of a t-type statistic and its null statistic, using kernel methods. A comparison of these two distributions by means of a likelihood ratio test can identify genes with significantly changed expressions. A method for the calculation of the cut-off point and the acceptance region is also derived. This methodology is applied to a leukemia data set containing expression levels of 7129 genes. The corresponding results are compared to the traditional t-test and the normal mixture model.
Q-Index Code B1
Institutional Status Non-UQ

Document type: Book Chapter
Collection: School of Mathematics and Physics
 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Thu, 19 Sep 2013, 19:57:38 EST by Kay Mackie on behalf of School of Mathematics & Physics