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Bioinformatics Advance Access published online on June 28, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm337
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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.

Improving gene quantification by adjustable spot-image restoration

Antonis Daskalakis 1,*, Dionisis Cavouras 2, Panagiotis Bougioukos 1, Spiros Kostopoulos 1, Dimitris Glotsos 1, Ioannis Kalatzis 2, George C. Kagadis 1, Christos Argyropoulos 1 and George Nikiforidis 1

1Medical Image Processing and Analysis (MIPA) Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 00 Rio, Greece
2Medical Image and Signal Processing Laboratory, Department of Medical Instruments Technology, Technological Educational Institute of Athens, 122 10 Athens, Greece

*To whom correspondence should be addressed. Antonis Daskalakis, E-mail: daskalakis{at}med.upatras.gr


  Abstract

Motivation: One of the major factors that complicate the task of microarray image analysis is that microarray images are distorted by various types of noise. In this study a robust framework is proposed, designed to take into account the effect of noise in microarray images in order to assist the demanding task of microarray image analysis. The proposed framework, incorporates in the microarray image processing pipeline a novel combination of spot adjustable image analysis and processing techniques and consists of the following stages: 1/ gridding for facilitating spot identification, 2/ clustering (unsupervised discrimination between spot and background pixels) applied to spot image for automatic local noise assessment, 3/ modeling of local image restoration process for spot image conditioning (adjustable wiener restoration using an empirically determined degradation function), 4/ automatic spot segmentation employing seeded-region-growing, 5/ intensity extraction, 6/ assessment of the reproducibility (real data) and the validity (simulated data) of the extracted gene expression levels.

Results: Both simulated and real microarray images were employed in order to assess the performance of the proposed framework against well established methods implemented in publicly available software packages (Scanalyze and SPOT). Regarding simulated images, the novel combination of techniques, introduced in the proposed framework, rendered the detection of spot areas and the extraction of spot intensities more accurate. Furthermore, on real images the proposed framework proved of better stability across replicates. Results indicate that the proposed framework improves spots’ segmentation and, consequently, quantification of gene expression levels.

Availability: All algorithms were implemented in MatlabTM (The Mathworks, Inc., Natick, MA) environment. The codes that implement microarray gridding, adaptive spot restoration and segmentation/intensity extraction are available upon request. Supplementary results and the simulated microarray images used in this study are available for download from: ftp://reviewers:bioinformatics@mipa.med.upatras.gr.

Associate Editor: Dr. Olga Troyanskaya

Received on February 9, 2007; revised on June 16, 2007; accepted on June 19, 2007

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