Segmentation and intensity estimation for microarray images with saturated pixels

BMC Bioinformatics

Volumn 12, Issue , 2011, Pages

  Yang, Yan ^a   Stafford, Phillip ^a   Kim, YoonJoo ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER MEMBERSHIPS; DETECTION THRESHOLD; DIAGNOSTIC ACCURACY; HIGH-LEVEL ANALYSIS; INTENSITY ESTIMATION; MICROARRAY IMAGE ANALYSIS; MODEL-BASED SEGMENTATION; SIGNAL SATURATION;

ESTIMATION; IMAGE SEGMENTATION; MICROARRAYS; MIXTURES;

PIXELS;

ANIMAL; ANIMAL DISEASE; ARTICLE; B CELL LYMPHOMA; COCCIDIOIDOMYCOSIS; COMPUTER PROGRAM; DOG; DOG DISEASE; HUMAN; IMAGE PROCESSING; MICROARRAY ANALYSIS; T CELL LYMPHOMA;

ANIMALS; COCCIDIOIDOMYCOSIS; DOG DISEASES; DOGS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LYMPHOMA, B-CELL; LYMPHOMA, T-CELL; MICROARRAY ANALYSIS; SOFTWARE;

EID: 82255173792     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-462     Document Type: Article

References (26)

1. Hsiao LL, Jensen RV, Yoshida T, Clark KE, Blumenstock JE, Gullans SR. Correcting for signal saturation errors in the analysis of microarray data. BioTechniques 2002, 32:330-336.
2. Dudley AM, Aach J, Steffen MA, Church GM. Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range. Proceedings of the National Academy of Sciences 2002, 99:7554-7559.
3. Lyng H, Badiee A, Svendsrud DH, Hovig E, Myklebost O, Stokke T. Profound influence of microarray scanner characteristics on gene expression ratios: Analysis and procedure for correction. BMC Genomics 2004, 5:10. 10.1186/1471-2164-5-10, 356910, 15018648.
4. Garcia de la NJ, van Hijum S, Trelles O. Saturation and quantization reduction in microarray experiments using two scans at different sensitivities. Statistical Applications in Genetics and Molecular Biology 2004, 3:Article 11.
5. Wit E, McClure J. Statistical adjustment of signal censoring in gene expression experiments. Bioinformatics 2003, 19:1055-1060. 10.1093/bioinformatics/btg003, 12801865.
6. Dodd LE, Korn EL, McShane LM, Chandramouli GVR, Chuang EY. Correcting log ratios for signal saturation in cDNA microarrays. Bioinformatics 2004, 20:2685-2693. 10.1093/bioinformatics/bth309, 15145811.
7. Ekstrom CT, Bak S, Kristensen C, Rudemo M. Spot shape modelling and data transformations for microarrays. Bioinformatics 2004, 20:2270-2278. 10.1093/bioinformatics/bth237, 15059816.
8. Khondoker MR, Glasbey CA, Worton BJ. Statistical estimation of gene expression using multiple laser scans of microarrays. Bioinformatics 2006, 22:215-219. 10.1093/bioinformatics/bti790, 16303798.
9. Glasbey CA, Forster T, Ghazal P. Estimation of expression levels in spotted microarrays with saturated pixels. Statistical Applications in Genetics and Molecular Biology 2007, 6:Article 34.
10. Gupta R, Auvinen P, Thomas A, Arjas E. Bayesian hierarchical model for correcting signal saturation in microarrays using pixel intensities. Statistical Applications in Genetics and Molecular Biology 2006, 5:Article 20.
11. Gupta R, Greco D, Auvinen P, Arjas E. Bayesian integrated modeling of expression data: A case study on RhoG. BMC Bioinformatics 2010, 11:295. 10.1186/1471-2105-11-295, 2894040, 20515463.
12. Chen Y, Dougherty ER, Bittner ML. Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics 1997, 2:364-374.
13. Glasbey CA, Ghazal P. Combinatorial image analysis of DNA microarray features. Bioinformatics 2003, 19:194-203. 10.1093/bioinformatics/19.2.194, 12538239.
14. Li Q, Fraley C, Bumgarner RE, Yeung KY, Raftery AE. Donuts, scratches and blanks: robust model-based segmentation of microarray images. Bioinformatics 2005, 21:2875-2882. 10.1093/bioinformatics/bti447, 15845656.
15. Baek J, Son YS, McLachlan GJ. Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinformatics 2007, 23:458-465. 10.1093/bioinformatics/btl630, 17166856.
16. Yang YH, Buckley MJ, Dudoit S, Speed TP. Comparison of methods for image analysis on cDNA microarray data. Journal of Computational and Graphical Statistics 2002, 11:108-136.
17. Pappaert K, Ottevaere H, Thienpont H, van Hummerlen P, Desmet G. Diffusion limitation: A possible source for the occurrence of doughnut patterns on DNA microarrays. BioTechniques 2006, 41:609-616. 10.2144/000112293, 17140119.
18. Legutki JB, Magee DM, Stafford P, Johnston SA. A general method for characterization of humoral immunity induced by a vaccine or infection. Vaccine 2010, 28:4529-4537. 10.1016/j.vaccine.2010.04.061, 20450869.
19. Halperin R, Stafford P, Johnston SA. Exploring antibody recognition of sequence space through random-sequence peptide microarrays. Molecular and Cellular Proteomics 2010, [First Published on November 9, 2010 as doi:10.1074/mcp.M110.000786].
20. Drǎghici S. Data Analysis Tools for DNA Microarrays 2003, Chapman & Hall/CRC.
21. Molecular Devices Corp GenePix Pro 6.0, User's Guide and Tutorial 2005, Molecular Devices, Corp, Molecular Devices Corp.
22. McLachlan G, Peel D. Finite Mixture Models 2000, John Wiley & Sons.
23. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society 1977, B39:1-38.
24. McLachlan G, Krishnan T. The EM Algorithm and Extensions 2008, Chapman & Hall/CRC.
25. Yang Y, Simpson DG. Unified computational methods for regression analysis of zero-inflated and bound-inflated data. Computational Statistics and Data Analysis 2010, 54:1525-1534. 10.1016/j.csda.2009.12.012, 2836522, 20228950.
26. Thisted RA. Elements of Statistical Computing: Numerical Computation 1988, Chapman and Hall/CRC.