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Cross-platform comparability of microarray data

ABSTRACT: To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms and methods to accommodate this variability. RNA expression data were generated in seven laboratories, comparing two standard RNA samples using twelve microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories dramatically increased when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Nonetheless, concordance could be found across different laboratories and platforms when data were analyzed in terms of enriched Gene Ontology categories. These findings indicate that microarray results generated by multiple sites and platforms can be comparable, and that multi-investigator teams will maximize data comparability by adopting a common platform and a common set of procedures to generate compatible data. Keywords: other

ORGANISM(S): Mus musculus

PROVIDER: GSE2458 | GEO | 2005/04/21

SECONDARY ACCESSION(S): PRJNA92677

REPOSITORIES: GEO

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Cross-platform comparability of microarray data

Project description:To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms and methods to accommodate this variability. RNA expression data were generated in seven laboratories, comparing two standard RNA samples using twelve microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories dramatically increased when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Nonetheless, concordance could be found across different laboratories and platforms when data were analyzed in terms of enriched Gene Ontology categories. These findings indicate that microarray results generated by multiple sites and platforms can be comparable, and that multi-investigator teams will maximize data comparability by adopting a common platform and a common set of procedures to generate compatible data. Keywords: other

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