MDQC: Mahalanobis Distance Quality Control

The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. Our Mahalanobis Distance Quality Control (MDQC) is a multivariate quality assessment method for microarrays that is based on the similarity of quality measures across arrays, i.e., on the idea of outlier detection. Intuitively, the “distance” of an array’s quality attributes measures the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. This method computes a distance measure, the Mahalanobis Distance, to summarize the quality measures contained in a quality control (QC) report for each array. The use of this distance allows us to take the correlation structure of the quality measures into account. In addition, by using robust estimators to identify the typical quality measures of good-quality arrays, the evaluation is not affected by the measures of outlying arrays.