ArticleNucleic acids research2009
Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips.
Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Ocular manifestations of central insulin resistance.Neural regeneration research · 2023Article
- The combination of everolimus and terameprocol exerts synergistic antiproliferative effects in endometrial cancer: molecular role of insulin-like growth factor binding protein 2.Journal of molecular medicine (Berlin, Germany) · 2018Article
- Estimating RNA-quality using GeneChip microarrays.BMC genomics · 2012Article
- Regulation of ovarian cancer progression by microRNA-187 through targeting Disabled homolog-2.Oncogene · 2012Article
- Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database.BMC bioinformatics · 2010Article
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Authors and funding
6 authors.
Funding
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Abstract
Interlaboratory comparison of microarray data, even when using the same platform, imposes several challenges to scientists. RNA quality, RNA labeling efficiency, hybridization procedures and data-mining tools can all contribute variations in each laboratory. In Affymetrix GeneChips, about 11-20 different 25-mer oligonucleotides are used to measure the level of each transcript. Here, we report that 'labeling extension values (LEVs)', which are correlation coefficients between probe intensities and probe positions, are highly correlated with the gene expression levels (GEVs) on eukaryotic Affymetrix microarray data. By analyzing LEVs and GEVs in the publicly available 2414 cel files of 20 Affymetrix microarray types covering 13 species, we found that correlations between LEVs and GEVs only exist in eukaryotic RNAs, but not in prokaryotic ones. Surprisingly, Affymetrix results of the same specimens that were analyzed in different laboratories could be clearly differentiated only by LEVs, leading to the identification of 'laboratory signatures'. In the examined dataset, GSE10797, filtering out high-LEV genes did not compromise the discovery of biological processes that are constructed by differentially expressed genes. In conclusion, LEVs provide a new filtering parameter for microarray analysis of gene expression and it may improve the inter- and intralaboratory comparability of Affymetrix GeneChips data.
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