Nucleic Acids Research Advance Access published online on December 7, 2006
Nucleic Acids Research, doi:10.1093/nar/gkl871
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Methods Online |
Positional artifacts in microarrays: experimental verification and construction of COP, an automated detection tool
1 Department of Molecular Biophysics and Biochemistry, Cellular and Developmental Biology, Yale University CT 06520, USA 2 Department of Molecular, Cellular and Developmental Biology, Yale University CT 06520, USA 3 Wilmer Institute, Johns Hopkins School of Medicine Baltimore, MD 21287, USA 4 Department of Computer Science 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA 5 Program in Computational Biology and Bioinformatics 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA
*To whom correspondence should be addressed. Tel: + 203 4325405; Fax: + 413 4102140; Email: mark.gerstein{at}yale.edu
Received January 9, 2006. Revised July 3, 2006. Accepted October 5, 2006.
Microarray technology is currently one of the most widely-used technologies in biology. Many studies focus on inferring the function of an unknown gene from its co-expressed genes. Here, we are able to show that there are two types of positional artifacts in microarray data introducing spurious correlations between genes. First, we find that genes that are close on the microarray chips tend to have higher correlations between their expression profiles. We call this the ‘chip artifact’. Our calculations suggest that the carry-over during the printing process is one of the major sources of this type of artifact, which is later confirmed by our experiments. Based on our experiments, the measured intensity of a microarray spot contains 0.1% (for fully-hybridized spots) to 93% (for un-hybridized ones) of noise resulting from this artifact. Secondly, we, for the first time, show that genes that are close on the microtiter plates in microarray experiments also tend to have higher correlations. We call this the ‘plate artifact’. Both types of artifacts exist with different severity in all cDNA microarray experiments that we analyzed. Therefore, we develop an automated web tool—COP (COrrelations by Positional artifacts) to detect these artifacts in microarray experiments. COP has been integrated with the microarray data normalization tool, ExpressYourself, which is available at http://bioinfo.mbb.yale.edu/ExpressYourself/. Together, the two can eliminate most of the common noises in microarray data.