Abstract

Motivation: Microarray results accumulated in public repositories are widely re-used in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment and efficient method for quality assessment is neces-sary to ensure their reliability. Results: The lack of a good benchmark has hampered evaluation of existing methods for quality control. In this study we propose a new inde-pendent quality metric that is based on evolutionary conservation of expression profiles. We show, using 11 large organ-specific datasets, that IQRray, a new quality metrics developed by us, exhibits the highest correlation with this reference metric, among 14 metrics tested. IQRray outperforms other methods in identification of poor quality arrays in dataset composed of arrays from many independent experiments. In con-trast, the performance of methods designed for detecting outliers in a single experiment like NUSE and RLE was low because of the inability of these method to detect datasets containing only low quality arrays, and the fact that the scores cannot be directly compared between ex-periments. Availability: The R implementation of IQRray is available at: this ftp URL

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