Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization

Microarray profiling of gene expression is a powerful tool for discovery, but the ability to manage and compare the resulting data can be problematic. Biological, experimental, and technical variations between studies of the same phenotype/phenomena create substantial differences in results. The app...

Full description

Saved in:

Bibliographic Details
Published in:	Gene Vol. 401; no. 1; pp. 12 - 18
Main Authors:	Cahan, Patrick, Rovegno, Felicia, Mooney, Denise, Newman, John C., St. Laurent, Georges, McCaffrey, Timothy A.
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 15-10-2007
Subjects:	Aging Animals Bioinformatics Computational Biology Data Interpretation, Statistical Databases, Genetic Expression profiling Gene annotation Gene expression Gene Expression Profiling Gene Expression Regulation Humans Meta-analysis Meta-Analysis as Topic Microarray Oligonucleotide Array Sequence Analysis - standards Species Specificity Transcript profiling AML NPC MIAME Expression profiling RPC Transcript profiling SAGE Microarray Gene expression Meta-analysis IFN DEGs ESC Aging ApoD LOLA SMART Bioinformatics Gene annotation
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Microarray profiling of gene expression is a powerful tool for discovery, but the ability to manage and compare the resulting data can be problematic. Biological, experimental, and technical variations between studies of the same phenotype/phenomena create substantial differences in results. The application of conventional meta-analysis to raw microarray data is complicated by differences in the type of microarray used, gene nomenclatures, species, and analytical methods. An alternative approach to combining multiple microarray studies is to compare the published gene lists which result from the investigators’ analyses of the raw data, as implemented in Lists of Lists Annotated (LOLA: www.lola.gwu.edu) and L2L (depts.washington.edu/l2l/). The present review considers both the potential value and the limitations of databasing and enabling the comparison of results from different microarray studies. Further, a major impediment to cross-study comparisons is the absence of a standard for reporting microarray study results. We propose a reporting standard: standard micro array results template (SMART), which will facilitate the integration of microarray studies.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-3 ObjectType-Review-1
ISSN:	0378-1119 1879-0038
DOI:	10.1016/j.gene.2007.06.016