Bayesian hierarchical models for serial analysis of gene expression

Seungyoon Nam, Seungmook Lee, Sanghyuk Lee, Seokmin Shin, Taesung Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the Serial Analysis of Gene Expression (SAGE) analysis, the statistical procedures have been performed after aggregation of observations from the various libraries for the same class. Most studies have not accounted for the within-class variability. The identification of the differentially expressed genes based on the class separation has not been easy because of heteroscedasticity of libraries. We propose a hierarchical Bayesian model that accounts for the within-class variability. The differential expression is measured by a distribution-free silhouette width which was first introduced into the SAGE differential expression analysis. It is shown that the silhouette width is more appropriate and is easier to compute than the error rate.

Original languageEnglish
Title of host publicationData Mining and Bioinformatics - First International Workshop, VDMB 2006, Revised Selected Papers
PublisherSpringer Verlag
Pages29-39
Number of pages11
ISBN (Print)3540689702, 9783540689706
DOIs
StatePublished - 2006
Event1st International Workshop on Data Mining and Bioinformatics, VDMB 2006 - Seoul, Korea, Republic of
Duration: 11 Sep 200611 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4316 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Data Mining and Bioinformatics, VDMB 2006
Country/TerritoryKorea, Republic of
CitySeoul
Period11/09/0611/09/06

Keywords

  • Bayesian hierarchical model
  • SAGE
  • Serial analysis of gene expression

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