Microarray analysis of gene expression: considerations in data mining and statistical treatment.
DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.
Davis Heart and Lung Research Institute, Department of Surgery, The Ohio State University, Columbus, Ohio, USA.
Joseph S
JS
Verducci
Vincent F
VF
Melfi
Shili
S
Lin
Zailong
Z
Wang
Sashwati
S
Roy
Chandan K
CK
Sen
D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D016247:Information Storage and Retrieval; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012984:Software