Article

A Markov Random Field model of microarray gridding

Authors:

Mathias Katzer,

Gerhard SagererAuthors Info & Claims

SAC '03: Proceedings of the 2003 ACM symposium on Applied computing

Pages 72 - 77

https://doi.org/10.1145/952532.952551

Published: 09 March 2003 Publication History

Abstract

DNA microarray hybridisation is a popular high through-put technique in academic as well as industrial functional genomics research. In this paper we present a new approach to automatic grid segmentation of the raw fluorescence microarray images by Markov Random Field (MRF) techniques. The main objectives are applicability to various types of array designs and robustness to the typical problems encountered in microarray images, which are contaminations and weak signal.We briefly introduce microarray technology and give some background on MRFs. Our MRF model of microarray gridding is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm. We show how to compute the model components efficiently and state our deterministic MRF energy minimization algorithm that was derived from the 'Highest Confidence First' algorithm by Chou et al. Since MRF segmentation may fail due to the properties of the data and the minimization algorithm, we use supplied or estimated print layouts to validate results.Finally we present results of tests on several series of microarray images from different sources, some of them test sets published with other microarray gridding software. Our MRF grid segmentation requires weaker assumptions about the array printing process than previously published methods and produces excellent results on many real datasets.An implementation of the described methods is available upon request from the authors.

References

[1]

D. H. Ballard. Computer Vision. Prentice-Hall, Englewood Cliffs, 10. edition, 1982.

Digital Library

[2]

A. Blake and A. Zisserman. Visual Reconstruction. MIT Press, Cambridge, Mass., 1987.

Digital Library

[3]

D. Bozinov and J. Rahnenführer. Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics, 18(5), 2002.

[4]

C. S. Brown., P. Goodwin, and P. Sorger. Image metrics in the statistical analysis of DNA microarray data. PNAS, 98(16):8944--8949, 2001.

[5]

J. Buhler, T. Ideker, and D. Haynor. Dapple: Improved Techiques for Finding Spots on DNA Microarrays. Technical Report UWTR 2000-08-05, University of Washington, 2000.

[6]

Y. Chen, E. R. Dougherty, and M. L. Bittner. Ratio-Based Decisions and the Quantitative Analysis of cDNA Microarray Images. J. Biomedical Optics, 2(4):364--374, 1997.

Digital Library

[7]

P. B. Chou, P. R. Cooper, M. J. Swain, C. M. Brown, and L. E. Wixson. Probabilistic Network Inference for Cooperative High and Low Level Vision. In R. Chellappa, editor, Markov random fields, pages 211--243. 1993.

[8]

M. Eisen and P. Brown. DNA arrays for analysis of gene expression. Methods in Enzymology, 303, 1999.

[9]

Gai, X., Lal, S., Xing, L., Brendel, V., and V. Walbot. Gene discovery using the maize genome database ZmDB. Nucleic Acids Research, (28):94--96, 2000.

[10]

A. Jain, T. Tokuyasu, A. Snijders, R. Segraves, D. Albertson, and D. Pinkel. Fully Automatic Quantification of Microarray Image Data. Genome Res., 12(2):325--332, 2002.

[11]

M. Katzer, F. Kummert, and G. Sagerer. Automatische Auswertung von Mikroarraybildern. In Workshop Bildverarbeitung für die Medizin, Leipzig, 2002.

[12]

R. Kindermann and J. L. Snell. Markov random fields and their applications. Contemporary Mathematics 1. American Mathematical Society, Providence, RI, 1980.

[13]

S. Z. Li. Markov random field modeling in image analysis. Computer science workbench. Springer, Tokyo, 2. edition, 2001.

Digital Library

[14]

G. Sherlock, T. Hernandez-Boussard, and A. Kasarskis. The Stanford Microarray Database. Nucleic Acids Research, (29):152--155, 2001.

[15]

M. Steinfath, W. Wruck, and H. Seidel. Automated image analysis for array hybridization experiments. Bioinformatics, 2001, Vol. 17, T. 7, S. 634--641, 2001.

[16]

Y. H. Yang, M. J. Buckley, S. Dudoit, and T. P. Speed. Comparison of Methods for Image Analysis on cDNA Microarray Data. Journal of Computational and Graphical Statistics, 11:108--136, 2002.

Cited By

Roopa CPriya MHarish BMaheshan M(2023)A Comprehensive Survey of Recent Approaches on Microarray Image DataSN Computer Science10.1007/s42979-023-02352-55:1Online publication date: 6-Dec-2023
https://doi.org/10.1007/s42979-023-02352-5
Joseph SSathidevi P(2020)A Fully Automated Gridding Technique for Real Composite cDNA Microarray ImagesIEEE Access10.1109/ACCESS.2020.29758558(39605-39622)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2975855
Saeid MNossair ZSaleh M(2019)A Fully Automated Spot Detection Approach for cDNA Microarray Images Using Adaptive Thresholds and Multi-Resolution AnalysisIEEE Access10.1109/ACCESS.2019.29235607(80380-80389)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2923560
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Published In

cover image ACM Conferences

SAC '03: Proceedings of the 2003 ACM symposium on Applied computing

March 2003

1268 pages

ISBN:1581136242

DOI:10.1145/952532

Conference Chair:
Gary B. Lamont
Air Force Institute of Technology
,
Program Chairs:
Hisham Haddad
Kennesaw State University
,
George A. Papadopoulos
University of Cyprus, Cyprus
,
Publications Chair:
Brajendra Panda
University of Arkansas

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 09 March 2003

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SAC03: ACM Symposium on Applied Computing

March 9 - 12, 2003

Florida, Melbourne

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Roopa CPriya MHarish BMaheshan M(2023)A Comprehensive Survey of Recent Approaches on Microarray Image DataSN Computer Science10.1007/s42979-023-02352-55:1Online publication date: 6-Dec-2023
https://doi.org/10.1007/s42979-023-02352-5
Joseph SSathidevi P(2020)A Fully Automated Gridding Technique for Real Composite cDNA Microarray ImagesIEEE Access10.1109/ACCESS.2020.29758558(39605-39622)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2975855
Saeid MNossair ZSaleh M(2019)A Fully Automated Spot Detection Approach for cDNA Microarray Images Using Adaptive Thresholds and Multi-Resolution AnalysisIEEE Access10.1109/ACCESS.2019.29235607(80380-80389)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2923560
Tennill TGross MFrieboes H(2017)Automated analysis of co-localized protein expression in histologic sections of prostate cancerPLOS ONE10.1371/journal.pone.017836212:5(e0178362)Online publication date: 26-May-2017
https://doi.org/10.1371/journal.pone.0178362
Datta AWai-Kin Kong AYow K(2016)A Fully Automatic Method for Gridding Bright Field Images of Bead-Based MicroarraysIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2015.243692820:4(1148-1159)Online publication date: Jul-2016
https://doi.org/10.1109/JBHI.2015.2436928
Ahmad MJambek Abin Mashor M(2014)A study on microarray image gridding techniques for DNA analysis2014 2nd International Conference on Electronic Design (ICED)10.1109/ICED.2014.7015793(171-175)Online publication date: Aug-2014
https://doi.org/10.1109/ICED.2014.7015793
Rueda LRezaeian I(2011)A fully automatic gridding method for cDNA microarray imagesBMC Bioinformatics10.1186/1471-2105-12-11312:1Online publication date: 21-Apr-2011
https://doi.org/10.1186/1471-2105-12-113
Rezaeian IRueda LGrossman RRzhetsky AKim SWang W(2011)Biological assessment of grid and spot detection in cDNA microarray imagesProceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine10.1145/2147805.2147807(12-19)Online publication date: 1-Aug-2011
https://dl.acm.org/doi/10.1145/2147805.2147807
Rueda LRezaeian I(2011)Applications of multilevel thresholding algorithms to transcriptomics dataProceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-642-25085-9_3(26-37)Online publication date: 15-Nov-2011
https://dl.acm.org/doi/10.1007/978-3-642-25085-9_3
Rezaeian IRueda L(2010)Sub-grid and spot detection in DNA microarray images using optimal multi-level thresholdingProceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics10.5555/1887854.1887882(277-288)Online publication date: 22-Sep-2010
https://dl.acm.org/doi/10.5555/1887854.1887882
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