Published online 27 August 2004
Nucleic Acids Research, Vol. 32 No. 15 © Oxford University Press 2004; all rights reserved
Comprehensive comparison of six microarray technologies
Mutagenesis Section, Environmental and Occupational Toxicology Division and 1 Biostatistics and Epidemiology Division, Health Canada, Ottawa, Ontario, Canada
* To whom correspondence should be addressed at Mutagenesis Section, Health Canada, Environmental Health Centre, P/L 0803A, Tunney's Pasture, Ottawa, ON, Canada K1A 0L2. Tel: +1 613 941 7376; Fax: +1 613 941 8530; Email: carole_yauk{at}hc-sc.gc.ca
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
Received June 18, 2004; Revised and Accepted August 13, 2004
Microarray technology is extensively used in biological research. The applied technologies vary greatly between laboratories, and outstanding questions remain regarding the degree of correlation among approaches. Recently, there has been a drive toward ensuring high-quality microarray data by the implementation of MIAME (Minimal Information About a Microarray Experiment) guidelines and an emphasis on ensuring public-availability to all datasets. However, despite its current widespread use and availability, very little is known about the extent to which application of the different technologies influences the outcome of transcriptional profiles and differential expression. The results among the handful of published studies are conflicting. Here, we present a comprehensive evaluation encompassing different reporter systems (short oligonucleotides, long oligonucleotides and cDNAs), labelling techniques and hybridization protocols. We used four oligonucleotide and two cDNA platforms to compare gene expression between two sample types. We determined the overall consistency (reproducibility) within each platform, and correlation among replicates within and between technologies. We find that the top performing platforms show low levels of technical variability that result in an increased ability to detect differential expression. Most importantly, we show the top four platforms are highly correlated with biological, rather than technological, differences accounting for the majority of variation in the data.
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