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Bayesian Error‐in‐Variable Survival Model for the Analysis of GeneChip Arrays
Author(s) -
Tadesse Mahlet G.,
Ibrahim Joseph G.,
Gentleman Robert,
Chiaretti Sabina,
Ritz Jerome,
Foa Robin
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00313.x
Subject(s) - bayesian probability , gene chip analysis , dna microarray , ranking (information retrieval) , identification (biology) , microarray analysis techniques , bayesian inference , computer science , posterior probability , computational biology , variable (mathematics) , expression (computer science) , data mining , statistics , bioinformatics , gene , biology , machine learning , genetics , artificial intelligence , gene expression , mathematics , mathematical analysis , botany , programming language
Summary DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the molecular basis for specific diseases. An intense area of research is concerned with the identification of genes related to particular phenotypes. The technology, however, is subject to various sources of error that may lead to expression readings that are substantially different from the true transcript levels. Few methods for microarray data analysis have accounted for measurement error in a substantial way and that is the purpose of this investigation. We describe a Bayesian error‐in‐variable model for the analysis of microarray data from a clinical study of patients with acute lymphoblastic leukemia. We focus in particular on the problem of identifying genes whose expression patterns are associated with duration of remission. This is a question of great practical interest since relapse is a major concern in the treatment of this disease. We explore the effects of ignoring the uncertainty in the expression estimates on the selection and ranking of genes.