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Published online 27 April 2004

Nucleic Acids Research, 2004, Vol. 32, No. 8 2323-2335

Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling

Musa H. Asyali*, Mohamed M. Shoukri, Omer Demirkaya and Khalid S. A. Khabar1

Department of Biostatistics, Epidemiology, and Scientific Computing and 1 Department of Biological and Medical Research, King Faisal Specialist Hospital and Research Center, PO Box 3354, MBC-03, Riyadh, 11211, Saudi Arabia

*To whom correspondence should be addressed. Tel: +966 1 464 7272, ext. 39211; Fax: +966 1 442 7854; Email: asyali{at}kfshrc.edu.sa

Received December 28, 2003; Revised March 15, 2004; Accepted March 24, 2004

DNA microarray is an important tool for the study of gene activities but the resultant data consisting of thousands of points are error-prone. A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. In this study, we describe an approach based on normal mixture modeling for determining optimal signal intensity thresholds to identify reliable measurements of the microarray elements and subsequently eliminate false expression ratios. We used univariate and bivariate mixture modeling to segregate the microarray data into two classes, low signal intensity and reliable signal intensity populations, and applied Bayesian decision theory to find the optimal signal thresholds. The bivariate analysis approach was found to be more accurate than the univariate approach; both approaches were superior to a conventional method when validated against a reference set of biological data that consisted of true and false gene expression data. Elimination of unreliable signal intensities in microarray data should contribute to the quality of microarray data including reproducibility and reliability of gene expression ratios.


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