Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis

Motivation: When analyzing microarray data, non-biological variation introduces uncertainty in the analysis and interpretation. In this paper we focus on the validation of significant differences in gene expression levels, or normalized channel intensity levels with respect to different experimental...

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Bibliographic Details
Published in:Bioinformatics 2004-02, Vol.20 (3), p.357-363
Main Authors: Kutalik, Zoltan, Inwald, Jacqueline, Gordon, Steve V., Hewinson, R. Glyn, Butcher, Philip, Hinds, Jason, Cho, Kwang-Hyun, Wolkenhauer, Olaf
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Data Interpretation, Statistical
DNA microarrays
Fundamental and applied biological sciences. Psychology
Gene Deletion
Gene Expression Profiling - methods
General aspects
Genetic Variation
Genome, Bacterial
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
Mycobacterium bovis
Mycobacterium bovis - genetics
Oligonucleotide Array Sequence Analysis - methods
Reproducibility of Results
Sample Size
Sensitivity and Specificity
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Summary:Motivation: When analyzing microarray data, non-biological variation introduces uncertainty in the analysis and interpretation. In this paper we focus on the validation of significant differences in gene expression levels, or normalized channel intensity levels with respect to different experimental conditions and with replicated measurements. A myriad of methods have been proposed to study differences in gene expression levels and to assign significance values as a measure of confidence. In this paper we compare several methods, including SAM, regularized t-test, mixture modeling, Wilk's lambda score and variance stabilization. From this comparison we developed a weighted resampling approach and applied it to gene deletions in Mycobacterium bovis. Results: We discuss the assumptions, model structure, computational complexity and applicability to microarray data. The results of our study justified the theoretical basis of the weighted resampling approach, which clearly outperforms the others. Availability: Algorithms were implemented using the statistical programming language R and available on the author's web-page. Supplementary information: For additional material see http://www.sbi.uni-rostock.de/
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btg417