ABSTRACT

There are numerous successful microarray experiments that have advanced our understanding of biology [1-5]. The important role of quality control (QC) in such successes is often overlooked. Successfully completing microarray experiments

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includes assessing the quality of the array design, the experimental design, the experimental execution, the data analysis, and the biological interpretation. At each step data quality and data integrity should be maintained by minimizing both systematic and random measurement errors. Data integrity is defined as the ability to faithfully identify each data point with its transcript sequence, annotation, and the experimental conditions that generated it. Data integrity is maintained through skilled design, collection, analysis, and annotation. Data integrity is maintained by ensuring the precise classification of transcripts, the application of appropriate analytical methods and the accurate interpretation of biological results. Data quality refers to the familiar technical and biological sources of error in the data. In this chapter we provide advice and a strategy for assessing and maintaining data quality and integrity at each stage of a microarray experiment. Special emphasis is given to the Affymetrix® GeneChip® microarray platform. However, many QC issues are universal and the implication to other platforms will be discussed.