Document Detail

Differential analysis of DNA microarray gene expression data.
MedLine Citation:
PMID:  12581345     Owner:  NLM     Status:  MEDLINE    
Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.
G Wesley Hatfield; She-Pin Hung; Pierre Baldi
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.; Review    
Journal Detail:
Title:  Molecular microbiology     Volume:  47     ISSN:  0950-382X     ISO Abbreviation:  Mol. Microbiol.     Publication Date:  2003 Feb 
Date Detail:
Created Date:  2003-02-12     Completed Date:  2003-05-01     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  8712028     Medline TA:  Mol Microbiol     Country:  England    
Other Details:
Languages:  eng     Pagination:  871-7     Citation Subset:  IM    
Department of Microbiology, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697, USA.
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MeSH Terms
Analysis of Variance
Bayes Theorem
Data Interpretation, Statistical
Escherichia coli / genetics
False Negative Reactions
False Positive Reactions
Gene Expression Profiling / statistics & numerical data*
Oligonucleotide Array Sequence Analysis / statistics & numerical data*
Grant Support

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