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; Soumitra Ghosh

doi:10.1093/bioinformatics/btg154

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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·Soumitra Ghosh

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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 q(com) that is defined for every spot, and the ratio distribution depends on q(com). 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 q(com)-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.

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Algorithms Computer Simulation False Positive Reactions Gene Expression Profiling Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Quality Control Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Statistics as Topic Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Xujing Wang, Martin J Hessner, Yan Wu, Nirupma Pati, Soumitra Ghosh. 2003-07-22. Quantitative quality control in microarray experiments and the application in data filtering, normalization and false positive rate prediction.. https://doi.org/10.1093/bioinformatics%2Fbtg154

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