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Bioinformatics Vol. 19 no. 11 2003
Pages 1341-1347
© 2003 Oxford University Press

Quantitative quality control in microarray experiments and the application in data filtering, normalization and false positive rate prediction

Xujing Wang *, Martin J. Hessner , Yan Wu , Nirupma Pati and Soumitra Ghosh

Max McGee National Research Center for Juvenile Diabetes, Department of Pediatrics, Medical College and Children’s Hospital of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA

Received on December 4, 2002 ; revised on January 27, 2003 ; accepted on January 29, 2003

Abstract: Data preprocessing including proper normalization and adequate quality control before complex data mining is crucial for studies using the cDNA microarray technology. We have developed a simple procedure that integrates data filtering and normalization with quantitative quality control of microarray experiments. Previously we have shown that data variability in a microarray experiment can be very well captured by a quality score qcom that is defined for every spot, and the ratio distribution depends on qcom. Utilizing this knowledge, our data-filtering scheme allows the investigator to decide on the filtering stringency according to desired data variability, and our normalization procedure corrects the qcom-dependent dye biases in terms of both the location and the spread of the ratio distribution. In addition, we propose a statistical model for false positive rate determination based on the design and the quality of a microarray experiment. The model predicts that a lower limit of 0.5 for the replicate concordance rate is needed in order to be certain of true positives. Our work demonstrates the importance and advantages of having a quantitative quality control scheme for microarrays.

Contact: xujing{at}mcw.edu

* To whom correspondence should be addressed.


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