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Bioinformatics Advance Access originally published online on October 12, 2007
Bioinformatics 2007 23(23):3162-3169; doi:10.1093/bioinformatics/btm487
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

MDQC: a new quality assessment method for microarrays based on quality control reports

Gabriela V. Cohen Freue 1,2,*, Zsuzsanna Hollander 6, Enqing Shen 6, Ruben H. Zamar 2, Robert Balshaw 2, Andreas Scherer 7, Bruce McManus 3,6, Paul Keown 4,8, W. Robert McMaster 5,8 and Raymond T. Ng 1

1Department of Computer Science, 2Department of Statistics, 3Department of Pathology and Laboratory Medicine, 4Department of Medicine, 5Department of Medical Genetics, University of British Columbia, 6The James Hogg iCAPTURE Centre, Providence Health Care-University of British Columbia, 7Novartis Pharma AG, Basel and 8Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada

*To whom correspondence should be addressed.


  Abstract

Motivation: 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. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems.

Results: We propose a novel multivariate approach to evaluate the quality of an array that examines the ‘Mahalanobis distance’ of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the method's ability to detect unusual arrays and helps to identify possible reasons of the quality problems.

Availability: The library to implement MDQC will soon be available from Bioconductor

Contact: gcohen{at}mrl.ubc.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

Received on June 6, 2007; revised on September 6, 2007; accepted on September 25, 2007

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