title: Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment.
abstract: Meta-analysis of microarray data is increasingly important, considering both the availability of multiple platforms using disparate technologies and the accumulation in public repositories of data sets from different laboratories. We addressed the issue of comparing gene expression profiles from two microarray platforms by devising a standardized investigative strategy. We tested this procedure by studying MDA-MB-231 cells, which undergo apoptosis on treatment with resveratrol. Gene expression profiles were obtained using high-density, short-oligonucleotide, single-color microarray platforms: GeneChip (Affymetrix) and CodeLink (Amersham). Interplatform analyses were carried out on 8414 common transcripts represented on both platforms, as identified by LocusLink ID, representing 70.8% and 88.6% of annotated GeneChip and CodeLink features, respectively. We identified 105 differentially expressed genes (DEGs) on CodeLink and 42 DEGs on GeneChip. Among them, only 9 DEGs were commonly identified by both platforms. Multiple analyses (BLAST alignment of probes with target sequences, gene ontology, literature mining, and quantitative real-time PCR) permitted us to investigate the factors contributing to the generation of platform-dependent results in single-color microarray experiments. An effective approach to cross-platform comparison involves microarrays of similar technologies, samples prepared by identical methods, and a standardized battery of bioinformatic and statistical analyses.
authors.affiliation: Institute of Biomedical Technologies, National Research Council, Milan, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Severgnini
authors.forename: Silvio
authors.initials: S
authors.lastname: Bicciato
authors.forename: Eleonora
authors.initials: E
authors.lastname: Mangano
authors.forename: Francesca
authors.initials: F
authors.lastname: Scarlatti
authors.forename: Alessandra
authors.initials: A
authors.lastname: Mezzelani
authors.forename: Michela
authors.initials: M
authors.lastname: Mattioli
authors.forename: Riccardo
authors.initials: R
authors.lastname: Ghidoni
authors.forename: Clelia
authors.initials: C
authors.lastname: Peano
authors.forename: Raoul
authors.initials: R
authors.lastname: Bonnal
authors.forename: Federica
authors.initials: F
authors.lastname: Viti
authors.forename: Luciano
authors.initials: L
authors.lastname: Milanesi
authors.forename: Gianluca
authors.initials: G
authors.lastname: De Bellis
authors.forename: Cristina
authors.initials: C
authors.lastname: Battaglia
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods; D018076:DNA, Complementary / Q000032:analysis; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000187:drug effects; D006801:Humans; D015201:Meta-Analysis as Topic; D020411:Oligonucleotide Array Sequence Analysis / Q000592:standards / Q000706:statistics & numerical data*; D016133:Polymerase Chain Reaction / Q000379:methods; D015203:Reproducibility of Results; D000077185:Resveratrol; D012680:Sensitivity and Specificity; D013267:Stilbenes / Q000494:pharmacology
chemical_list: D018076:DNA, Complementary; D013267:Stilbenes; D000077185:Resveratrol