About this Journal Submit a Manuscript Table of Contents
Advances in Bioinformatics
Volume 2013 (2013), Article ID 167915, 9 pages
http://dx.doi.org/10.1155/2013/167915
Research Article

Correction of Spatial Bias in Oligonucleotide Array Data

1Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, C.P. 6128, Succursale Centre-Ville, Montréal, QC, Canada H3C 3J7
2Department of Computer Science and Operations Research, Université de Montréal, C.P. 6128, Succursale Centre-Ville, Montréal, QC, Canada H3C 3J7

Received 6 July 2012; Accepted 2 February 2013

Academic Editor: Tatsuya Akutsu

Copyright © 2013 Philippe Serhal and Sébastien Lemieux. 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.

Linked References

  1. M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, “Quantitative monitoring of gene expression patterns with a complementary DNA microarray,” Science, vol. 270, no. 5235, pp. 467–470, 1995. View at Scopus
  2. R. Edgar, M. Domrachev, and A. E. Lash, “Gene Expression Omnibus: NCBI gene expression and hybridization array data repository,” Nucleic Acids Research, vol. 30, no. 1, pp. 207–210, 2002. View at Scopus
  3. D. J. Lockhart, H. Dong, M. C. Byrne et al., “Expression monitoring by hybridization to high-density oligonucleotide arrays,” Nature Biotechnology, vol. 14, no. 13, pp. 1675–1680, 1996. View at Scopus
  4. D. W. Selinger, K. J. Cheung, R. Mei et al., “RNA expression analysis using a 30 base pair resolution Escherichia coli genome array,” Nature Biotechnology, vol. 18, no. 12, pp. 1262–1268, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. A. J. Hartemink, D. K. Gifford, T. S. Jaakkola, and R. A. Young, “Maximum likelihood estimation of optimal scaling factors for expression array normalization,” Microarrays: Optical Technologies and Informatics, vol. 2, no. 23, pp. 132–140, 2001.
  6. L. M. Cope, R. A. Irizarry, H. A. Jaffee, Z. Wu, and T. P. Speed, “A benchmark for Affymetrix GeneChip expression measures,” Bioinformatics, vol. 20, no. 3, pp. 323–331, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Gentleman, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer Science and Business Media, New York, NY, USA, 2005.
  8. F. Naef and M. O. Magnasco, “Solving the riddle of the bright mismatches: labeling and effective binding in oligonucleotide arrays,” Physical Review E, vol. 68, no. 1, part 1, Article ID 011906, 2003. View at Scopus
  9. Z. Wu and R. A. Irizarry, “Stochastic models inspired by hybridization theory for short oligonucleotide arrays,” Journal of Computational Biology, vol. 12, no. 6, pp. 882–893, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Reimers and J. N. Weinstein, “Quality assessment of microarrays: visualization of spatial artifacts and quantitation of regional biases,” BMC Bioinformatics, vol. 6, article 166, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Suárez-Fariñas, A. Haider, and K. M. Wittkowski, “"Harshlighting" small blemishes on microarrays,” BMC Bioinformatics, vol. 6, article 65, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. G. J. G. Upton and J. C. Lloyd, “Oligonucleotide arrays: information from replication and spatial structure,” Bioinformatics, vol. 21, no. 22, pp. 4162–4168, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. J. M. Arteaga-Salas, A. P. Harrison, and G. J. G. Upton, “Reducing spatial flaws in oligonucleotide arrays by using neighborhood information,” Statistical Applications in Genetics and Molecular Biology, vol. 7, no. 1, article 29, 2008. View at Scopus
  14. W. B. Langdon, G. J. Upton, R. da Silva Camargo, and A. P. Harrison, “A survey of spatial defects in Homo Sapiens Affymetrix GeneChips,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 4, pp. 647–653, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Z. Gharaibeh, A. A. Fodor, and C. J. Gibas, “Software note: using probe secondary structure information to enhance Affymetrix GeneChip background estimates,” Computational Biology and Chemistry, vol. 31, no. 2, pp. 92–98, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. V. G. Ratushna, J. W. Weller, and C. J. Gibas, “Secondary structure in the target as a confounding factor in synthetic oligomer microarray design,” BMC Genomics, vol. 6, article 31, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Wei, P. F. Kuan, S. Tian et al., “A study of the relationships between oligonucleotide properties and hybridization signal intensities from NimbleGen microarray datasets,” Nucleic Acids Research, vol. 36, no. 9, pp. 2926–2938, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. M. P. Samanta, W. Tongprasit, H. Sethi, C. Chin, and V. Stolc, “Global identification of noncoding RNAs in Saccharomyces cerevisiae by modulating an essential RNA processing pathway,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 11, pp. 4192–4197, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. G. J. G. Upton, O. Sanchez-Graillet, J. Rowsell et al., “On the causes of outliers in Affymetrix GeneChip data,” Briefings in Functional Genomics and Proteomics, vol. 8, no. 3, pp. 199–212, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. A. A. Ahmed, M. Vias, N. G. Iyer, C. Caldas, and J. D. Brenton, “Microarray segmentation methods significantly influence data precision,” Nucleic Acids Research, vol. 32, no. 5, article e50, 2004. View at Scopus
  21. J. T. Leek and J. D. Storey, “Capturing heterogeneity in gene expression studies by surrogate variable analysis,” PLoS Genetics, vol. 3, no. 9, pp. 1724–1735, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. R. A. Irizarry, B. Hobbs, F. Collin et al., “Exploration, normalization, and summaries of high density oligonucleotide array probe level data,” Biostatistics, vol. 4, no. 2, pp. 249–264, 2003. View at Scopus
  23. F. Naef, D. A. Lim, N. Patil, and M. Magnasco, “DNA hybridization to mismatched templates: a chip study,” Physical Review E, vol. 65, no. 4, part 1, Article ID 040902, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. Affymetrix, “GeneChip Gene 1.0 ST Array System,” Santa Clara, Calif, USA, 2007.
  25. Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed, “Normalization for cDNA microarray data,” Microarrays: Optical Technologies and Informatics, vol. 2, no. 23, pp. 141–152, 2001.
  26. S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed, “Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments,” Statistica Sinica, vol. 12, no. 1, pp. 111–139, 2002. View at Scopus
  27. B. M. Bolstad, R. A. Irizarry, M. Astrand, and T. P. Speed, “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias,” Bioinformatics, vol. 19, no. 2, pp. 185–193, 2003. View at Publisher · View at Google Scholar · View at Scopus
  28. J. A. Berger, S. Hautaniemi, A. Järvinen, H. Edgren, S. K. Mitra, and J. Astola, “Optimized LOWESS normalization parameter selection for DNA microarray data,” BMC Bioinformatics, vol. 5, article 194, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. M. E. Ritchie, J. Silver, A. Oshlack et al., “A comparison of background correction methods for two-colour microarrays,” Bioinformatics, vol. 23, no. 20, pp. 2700–2707, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. S. L. Carter, A. C. Eklund, B. H. Mecham, I. S. Kohane, and Z. Szallasi, “Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements,” BMC Bioinformatics, vol. 6, article 107, 2005. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Wu, R. Carta, and Zhang, “Sequence dependence of cross-hybridization on short oligo microarrays,” Nucleic Acids Research, vol. 33, no. 9, p. e84, 2005. View at Scopus
  32. H. Binder, J. Brücker, and C. J. Burden, “Nonspecific hybridization scaling of microarray expression estimates: a physicochemical approach for chip-to-chip normalization,” Journal of Physical Chemistry B, vol. 113, no. 9, pp. 2874–2895, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. H. Yang, S. Dudoit, P. Luu et al., “Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation,” Nucleic Acids Research, vol. 30, no. 4, p. e15, 2002. View at Scopus
  34. C. Workman, L. J. Jensen, H. Jarmer et al., “A new non-linear normalization method for reducing variability in DNA microarray experiments,” Genome Biology, vol. 3, no. 9, research0048, 2002. View at Scopus
  35. C. Colantuoni, G. Henry, S. Zeger, and J. Pevsner, “Local mean normalization of microarray element signal intensities across an array surface: quality control and correction of spatially systematic artifacts,” BioTechniques, vol. 32, no. 6, pp. 1316–1320, 2002. View at Scopus
  36. D. L. Wilson, M. J. Buckley, C. A. Helliwell, and I. W. Wilson, “New normalization methods for cDNA microarray data,” Bioinformatics, vol. 19, no. 11, pp. 1325–1332, 2003. View at Publisher · View at Google Scholar · View at Scopus
  37. D. Baird, P. Johnstone, and T. Wilson, “Normalization of microarray data using a spatial mixed model analysis which includes splines,” Bioinformatics, vol. 20, no. 17, pp. 3196–3205, 2004. View at Publisher · View at Google Scholar · View at Scopus
  38. A. L. Tarca, J. E. Cooke, and J. Mackay, “A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data,” Bioinformatics, vol. 21, no. 11, pp. 2674–2683, 2005. View at Publisher · View at Google Scholar · View at Scopus
  39. P. Neuvial, P. Hupé, I. Brito et al., “Spatial normalization of array-CGH data,” BMC Bioinformatics, vol. 7, article 264, 2006. View at Publisher · View at Google Scholar · View at Scopus
  40. H. S. Chai, T. M. Therneau, K. R. Bailey, and J. A. Kocher, “Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays,” BMC Bioinformatics, vol. 11, article 356, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. J. M. Arteaga-Salas, H. Zuzan, W. B. Langdon, G. J. G. Upton, and A. P. Harrison, “An overview of image-processing methods for affymetrix genechips,” Briefings in Bioinformatics, vol. 9, no. 1, pp. 25–33, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. T. H. Stokes, R. A. Moffitt, J. H. Phan, and M. D. Wang, “Chip artifact CORRECTion (caCORRECT): a bioinformatics system for quality assurance of genomics and proteomics array data,” Annals of Biomedical Engineering, vol. 35, no. 6, pp. 1068–1080, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. R. A. Irizarry, Z. Wu, and H. A. Jaffee, “Comparison of Affymetrix GeneChip expression measures,” Bioinformatics, vol. 22, no. 7, pp. 789–794, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. N. O. Stitziel, B. G. Mar, J. Liang, and C. A. Westbrook, “Membrane-associated and secreted genes in breast cancer,” Cancer Research, vol. 64, no. 23, pp. 8682–8687, 2004. View at Publisher · View at Google Scholar · View at Scopus
  45. D. Magda, P. Lecane, R. A. Miller et al., “Motexafin gadolinium disrupts zinc metabolism in human cancer cell lines,” Cancer Research, vol. 65, no. 9, pp. 3837–3845, 2005. View at Publisher · View at Google Scholar · View at Scopus
  46. “Latin Square Data for Expression Algorithm Assessment,” http://www.affymetrix.com/support/technical/sample_data/datasets.affx.
  47. C. Cheng and L. M. Li, “Sub-array normalization subject to differentiation,” Nucleic Acids Research, vol. 33, no. 17, pp. 5565–5573, 2005. View at Publisher · View at Google Scholar · View at Scopus
  48. C. Li and W. H. Wong, “Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 1, pp. 31–36, 2001. View at Publisher · View at Google Scholar · View at Scopus
  49. B. Bolstad, J. Brettschneider, K. Simpson, L. Cope, R. Irizarry, and T. P. Speed, “Quality assessment of affymetrix GeneChip data,” in Bioinformatics and Computational Biology Using R and Bioconductor, R. Gentleman, V. Carey, W. Huber, R. Irizarry, and S. Dudoit, Eds., Springer, 2005.
  50. R. C. Geary, “The contiguity ratio and statistical mapping,” The Incorporated Statistician, vol. 5, no. 3, pp. 115–146, 1954.
  51. P. A. Moran, “Notes on continuous stochastic phenomena,” Biometrika, vol. 37, no. 1-2, pp. 17–23, 1950. View at Scopus
  52. Z. Wu, R. A. Irizarry, R. Gentleman, F. Martinez-Murillo, and F. Spencer, “A model-based background adjustment for oligonucleotide expression arrays,” Journal of the American Statistical Association, vol. 99, no. 468, pp. 909–917, 2004. View at Publisher · View at Google Scholar · View at Scopus
  53. B. M. Bolstad, Low-level analysis of high-density oligonucleotide array data: background, normalization and summarization [Ph.D. thesis in biostatistics], University of California, Berkeley, 2004.