Nucleic Acids Research Advance Access originally published online on March 18, 2009
Nucleic Acids Research 2009 37(8):e61; doi:10.1093/nar/gkp168
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Nucleic Acids Research, 2009, Vol. 37, No. 8 e61
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Methods Online |
Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips
1Department of Biotechnology, Ming Chuan University, 2Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Tao-Yuan, 3Institute of Statistical Science, Academia Sinica, Taipei, 4Graduate Institutes of Clinical Medical Sciences, Chang Gung University and 5Department of Obstetrics and Gynecology, Lin-Kou Medical Center, Chang Gung Memorial Hospital and Chang Gung University, Tao-Yuan, Taiwan
*To whom correspondence should be addressed. Tel: +886 3 3281200 extension 5402; Fax: +886 3 3288252; Email: knoxtn{at}cgmh.org.tw Correspondence may also be addressed to Angel Chao. Email: angel945{at}cgmh.org.tw
Received November 20, 2008. Revised February 24, 2009. Accepted March 2, 2009.
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 eukayotic 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 [NCBI GEO] , 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.