Skip to main content
EURASIP Journal on Advances in Signal Processing
Download PDF
  • Research Article
  • Open access
  • Published:

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 080163 (2006) Cite this article

Abstract

This paper overviews DNA microarray grid alignment and foreground separation approaches. Microarray grid alignment and foreground separation are the basic processing steps of DNA microarray images that affect the quality of gene expression information, and hence impact our confidence in any data-derived biological conclusions. Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis. In the past, the grid alignment and foreground separation steps have not been covered extensively in the survey literature. We present several classifications of existing algorithms, and describe the fundamental principles of these algorithms. Challenges related to automation and reliability of processed image data are outlined at the end of this overview paper.

References

  1. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with complementary DNA microarray. Science 1995, 270: 467–470. 10.1126/science.270.5235.467

    Article  Google Scholar 

  2. Fenstemacher D: Introduction to bioinformatics. Journal of the American Society for Information Science and Technology 2005, 65(5):440–446.

    Article  Google Scholar 

  3. MacMullen WJ, Denn SO: Information problems in molecular biology and bioinformatics. Journal of the American Society for Information Science and Technology 2005, 65(5):447–456.

    Article  Google Scholar 

  4. Quackenbush J: Computational analysis of microarray. Computational Analysis of Microarray 2001, 2(6):418–427.

    Google Scholar 

  5. Bajcsy P, Han J, Liu L, Young J: Survey of bioData analysis from data mining perspective. In Data Mining in Bioinformatics. Edited by: Wang JTL, Zaki MJ, Toivonen HTT, Shasha D. Springer, New York, NY, USA; 2004:9–39. chapter 2

    Google Scholar 

  6. Baldi P, Brunak S: Bioinformatics, The Machine Learning Approach. 2nd edition. The MIT Press, Cambridge, Mass, USA; 2001.

    MATH  Google Scholar 

  7. Golub TR, Slonim DK, Tamayo P, et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531–537. 10.1126/science.286.5439.531

    Article  Google Scholar 

  8. Moore SK: Understanding the human genome. IEEE Spectrum 2000, 37(11):33–42. 10.1109/6.880951

    Article  Google Scholar 

  9. Goryachev AB, MacGregor PF, Edwards AM: Unfolding of microarray data. Journal of Computational Biology 2001, 8(4):443–461. 10.1089/106652701752236232

    Article  Google Scholar 

  10. Bajcsy P: An overview of microarray image processing requirements. The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), the Workshop on Computer Vision Methods for Bioinformatics (CVMB), June 2005, San Diego, Calif, USA

    Google Scholar 

  11. Brazma A, Hungamp P, Quackenbush J, et al.: Minimum information about a microarray experiment (MIAME) - toward standards for microarray data. Nature Genetics 2001, 29(4):365–371. 10.1038/ng1201-365

    Article  Google Scholar 

  12. Kamberova G, Shah S (Eds): DNA Array Image Analysis - Nuts and Bolts. Data Analysis Tools for DNA Microarrays. DNA Press LLC, Salem, Mass, USA; 2002.

    Google Scholar 

  13. Srinark T, Kambhamettu C: A microarray image analysis system based on multiple-snake. Journal of Biological Systems 2004., 12(4): Special issue

    MATH  Google Scholar 

  14. Yue H, Eastman PS, Wang BB, et al.: An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression. Nucleic Acids Research 2001, 29(8):e41–1.

    Article  Google Scholar 

  15. Draghici S: Data Analysis Tools for DNA Microarrays, CRC Mathematical Biology and Medicine Series. Chapman & Hall, London, UK; 2003.

    Book  Google Scholar 

  16. Han J, Kamber M: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco, Calif, USA; 2001.

    MATH  Google Scholar 

  17. Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg Å, Peterson C: BioArray software environment: a platform for comprehensive management and analysis of microarray data. Genome Biology 2002., 3(8): software 0003.1-0003.6

    Google Scholar 

  18. Samartzidou H, Turner L, Houts T: Lucidea Microarray ScoreCard: An integrated tool for validation of microarray gene expression experiments," Innovation Forum, Microarrays. Life Science News 8, 2001 Amersham Pharmacia Biotech

    Google Scholar 

  19. Rocke D, Durbin B: A model for measurement error for gene expression arrays. Journal of Computational Biology 2001, 8(6):557–569. 10.1089/106652701753307485

    Article  Google Scholar 

  20. Seo J, Shneiderman B: Interactively exploring hierarchical clustering results. IEEE Computer 2002, 35(7):80–86. 10.1109/MC.2002.1016905

    Article  Google Scholar 

  21. Balagurunathan Y, Dougherty ER, Chen Y, Bittner ML, Trent JM: Simulation of cDNA microarrays via a parameterized random signal model. Journal of Biomedical Optics 2002., 7(3):

    Article  Google Scholar 

  22. Brandle N, Bischof H, Lapp H: Robust DNA Microarray image analysis. Machine Vision and Applications 2003, 15(1):11–28. 10.1007/s00138-002-0114-x

    Article  Google Scholar 

  23. Whitfield CW, Cziko AM, Robinson GE: Gene expression profiles in the brain predict behavior in individual honey bees. Science 2003, 302: 296–299. 10.1126/science.1086807

    Article  Google Scholar 

  24. Bajcsy P: Gridline: automatic grid alignment in DNA microarray scans. IEEE Transactions on Image Processing 2004, 13(1):15–25. 10.1109/TIP.2003.819941

    Article  MathSciNet  Google Scholar 

  25. Jung H-Y, Cho H-G: An automatic block and spot indexing with k-nearest neighbors graph for microarray image analysis. Bioinformatics 2002, 18(2):S141–S151. 10.1093/bioinformatics/18.suppl_2.S141

    Article  Google Scholar 

  26. Axon Instruments Inc : GenePix Pro, Product Description. https://doi.org/www.axon.com/GN_Genomics.html

  27. Eisen M: ScanAlyze. Product Description at https://doi.org/rana.lbl.gov/EisenSoftware.htm

  28. Scanalytics Inc : MicroArray Suite. Product Description at https://doi.org/www.scanalytics.com/product/hts/microarray.html

  29. Buhler J, Ideker T, Haynor D: Dapple: improved techniques for finding spots on DNA microarrays. In Tech. Rep. UWTR 2000-08-05. UV CSE, Seattle, Wash, USA;

  30. Biodiscovery Inc : ImaGene Product description. 2005.https://doi.org/www.biodiscovery.com/imagene.asp

    Google Scholar 

  31. Packard BioChip Technologies, LLC, "Quant Array Analysis Software," Product Description at https://doi.org/las.perkinelmer.com/Content/RelatedMaterials/ReflectionTechNote.pdf

  32. Imaging Research Inc : Array Vision. Product Description at https://doi.org/www.imagingresearch.com/products/Genomics_Software.asp

  33. Hartelius K, Cartstensen JM: Bayesian grid matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(2):162–173. 10.1109/TPAMI.2003.1177149

    Article  Google Scholar 

  34. Bajcsy P: Image To Knowledge (I2K). Software Documentation at https://doi.org/isda.ncsa.uiuc.edu/i2kmanual/

  35. CSIRO Mathematical and Informational Sciences : SpotImage Analysis Software. Product Documentation at https://doi.org/experimental.act.cmis.csiro.au/Spot/index.php

  36. Jain AN, Tokuyasu TA, Snijders AM, Segraves R, Albertson DG, Pinkel D: Fully automated quantification of microarray image data. Genome Research 2002, 12(2):325–332. 10.1101/gr.210902

    Article  Google Scholar 

  37. Steinfath M, Wruck W, Seidel H, Lehrach H, Radelof U, O'Brien J: Automated image analysis for array hybridization experiments. Bioinformatics 2001, 17(7):634–641. 10.1093/bioinformatics/17.7.634

    Article  Google Scholar 

  38. Katzer M, Kummert F, Sagerer G: Robust automatic microarray image analysis. Proceedings of the International Conference on Bioinformatics: North-South Networking, 2002, Bangkok, Thailand

    Google Scholar 

  39. Katzer M, Kummert F, Sagerer G: Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience 2003, 2(4):202–212. 10.1109/TNB.2003.817023

    Article  Google Scholar 

  40. Liew AW-C, Yan H, Yang M: Robust adaptive spot segmentation of DNA microarray images. Pattern Recognition 2003, 36(5):1251–1254. 10.1016/S0031-3203(02)00170-X

    Article  Google Scholar 

  41. Russ J: The Image Processing Handbook. 3rd edition. CRC Press LLC, Boca Raton, Fla, USA; 1999.

    MATH  Google Scholar 

  42. Angulo J, Serra J: Automatic analysis of DNA microarray images using mathematical morphology. Bioinformatics 2003, 19(5):553–562. 10.1093/bioinformatics/btg057

    Article  Google Scholar 

  43. Hirata R, Barrera J, Hashimoto RF, Dantas DO: Microarray gridding by mathematical morphology. Proceedings of 14th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI '01), October 2001, Florianopolis, Brazil 112–119.

    Chapter  Google Scholar 

  44. Antoniol G, Ceccarelli M: A markov random field approach to microarray image gridding. Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04) , August 2004, Cambridge, UK

    Google Scholar 

  45. Demirkaya O, Asyali MH, Shoukri MM: Segmentation of cDNA microarray spots using Markov random field modeling. Bioinformatics 2005, 21(13):2994–3000. 10.1093/bioinformatics/bti455

    Article  Google Scholar 

  46. Jin H-J, Chun B-K, Cho HG: Extended epsilon regular sequence for automated analysis of microarray images. The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), the Workshop on Computer Vision Methods for Bioinformatics (CVMB), June 2005, San Diego, Calif, USA

    Google Scholar 

  47. Bozinov D, Rahnenfuhrer J: Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics 2002, 18(5):747–756. 10.1093/bioinformatics/18.5.747

    Article  Google Scholar 

  48. Tou JT, Gonzales RC: Pattern Recognition Principles. Addison-Wesley, Reading, Mass, USA; 1974.

    Google Scholar 

  49. Rahnenführer J, Bozinov D: Hybrid clustering for microarray image analysis combining intensity and shape features. BMC Bioinformatics 2004, 5(1):47. 10.1186/1471-2105-5-47

    Article  Google Scholar 

  50. Chen Y, Dougherty ER, Bittner ML: Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal Of Biomedical Optics 1997, 2(4):364–374. 10.1117/12.281504

    Article  Google Scholar 

  51. Sheskin DJ: Handbook of Parametric and Nonparametric Statistical Procedures. 2nd edition. Chapman & Hall CRC, London, UK; 2000.

    MATH  Google Scholar 

  52. Lukac R, Plataniotis KN, Smolka B, Venetsanopoulos AN: An automated multichannel procedure for cDNA microarray image processing. Lecture Notes in Computer Science 2004, 3212: 1–8. 10.1007/978-3-540-30126-4_1

    Article  Google Scholar 

  53. Adams RM, Stancampiano B, McKenna M, Small D: Case study: a virtual environment for genomic data visualization. IEEE Transactions on Visualization 2002., 1: October 27–November 1, 2002, Boston, Mass, USA (published as CD)

    Google Scholar 

  54. Lawrence ND, Milo M, Niranjan M, Rashbass P, Soullier S: Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics 2004, 20(4):518–526. 10.1093/bioinformatics/btg438

    Article  Google Scholar 

  55. Foster I, Kesselman C: Computational grids. In The Grid: Blueprint for a New Computing Infrastructure. Morgan-Kaufman, San Francisco, Calif, USA; 1999. chapter 2

    Google Scholar 

  56. Karo M, Dwan C, Freeman J, Weissman J, Livny M, Retzel E: Applying grid technologies to bioinformatics. Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing (HPDC '01), August 2001, San Francisco, Calif, USA 441–442.

    Chapter  Google Scholar 

  57. Strom CM, Clark DD, Hantash FM, et al.: Direct visualization of cystic fibrosis transmembrane regulator mutations in the clinical laboratory setting. Clinical Chemistry 2004, 50(5):836–845. 10.1373/clinchem.2003.026088

    Article  Google Scholar 

  58. Report of the National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure, https://doi.org/www.nsf.gov/od/oci/reports/toc.jsp

Download references

Author information

Authors and Affiliations

  1. The National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, IL, 61801, USA

    Peter Bajcsy

Authors
  1. Peter Bajcsy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Bajcsy.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Bajcsy, P. An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches. EURASIP J. Adv. Signal Process. 2006, 080163 (2006). https://doi.org/10.1155/ASP/2006/80163

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/ASP/2006/80163

Keywords