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Comparative and Functional Genomics
Volume 4, Issue 5, Pages 460-467
http://dx.doi.org/10.1002/cfg.317
Primary research paper

Post-Normalization Quality Assessment Visualization of Microarray Data

Department of Statistics, University of Glasgow, Glasgow G12 8QW, UK

Received 1 March 2003; Revised 17 June 2003; Accepted 22 July 2003

Copyright © 2003 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Post-normalization checking of microarrays rarely occurs, despite the problems that using unreliable data for inference can cause. This paper considers a number of different ways to check microarrays after normalization for a variety of potential problems. Four types of problem with microarray data that these checks can identify are: clerical mistakes, array-wide hybridization problems, problems with normalization and mishandling problems. Any of these can seriously affect the results of any analysis. The three main techniques used to identify these problems are dimension reduction techniques, false array plots and correlograms. None of the techniques are computationally very intensive and all can be carried out in the R statistical package. Once discovered, problems can either be rectified or excluded from the data.