Stochastic models inspired by hybridization theory for short oligonucleotide arrays

Proceedings of the Annual International Conference on Computational Molecular Biology, RECOMB

Volumn 8, Issue , 2004, Pages 98-106

  Wu, Zhijin ^a   Irizarry, Rafael A ^a  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Affymetrix probe level data; Background adjustment; Microarrays; Physical models; Stochastic models

Indexed keywords

ARRAYS; COMPUTER SOFTWARE; GENES; GENETIC ALGORITHMS; GENETIC ENGINEERING; IMAGE CODING; PLASMA PROBES; RNA;

GENE EXPRESSION; GENETIC HYBRIDIZATION; MOLECULAR HYBRIDIZATION THEORY; NON SPECIFIC HYBRIDIZATION (NSH); OLIGONUCLEOTIDES;

GENE TRANSFER;

EID: 2442453593     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/974614.974628     Document Type: Conference Paper

References (25)

1. R. E. Barlow and H. D. Brunk. The isotonic regression problem and its dual. Journal of the American Statistical Association, 67:140-147, 1972.
2. N. Blades, J. Jones, S. Kern, and G. Parmigiani. Denoising of data from serial analysis of gene expression. Bioinformatics, page To appear., 2003.
3. B. Bolstad, R. A. Irizarry, M. Astrand, and T. Speed. A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics, 19(2), 2003.
4. T. Chu, W. B., and R. Wolfinger. A systematic statistical linear modeling approach to oligonucleotide array experiments. Math Biosci, 176:35-51, 2002.
5. L. Cope, R. Irizarry, H. Jaffee, Z. Wu, and T. Speed. A benchmark for affymetrix genechip expression measures. Bioinformatics, page http://www.biostat.jhsph.edu/ririzarr/papers/index.html, 2003. In press.
6. X. Cui, M. K. Kerr, and G. A. Churchill. Data transformations for cDNA microarray data. Statistical Applications in Genetics and Molecular Biology, 2:Article 4, 2003.
7. 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, 12(1):111-139, 2002.
8. B. P. Durbin, J. S. Hardin, D. M. Hawkins, and D. M. Rocke. A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics, 18(Suppl. 1):S105-S110, 2002.
9. D. Hekstra, A. R. Taussig, M. Magnasco, and F. Naef. Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide array. Nucleic Acids Research, 31 (7): 1962-1968, 2003.
10. D. Holder, R. F. Raubertas, B. V. Pikounis, V. Svetnik, and K. Soper. Statistical analysis of high density oligonucleotide arrays: a SAFER approach. In Proceedings of the ASA Annual Meeting, Atlanta, GA 2001, 2001.
11. W. Huber, A. von Heydebreck, H. Sultmann, A. Poustka, and M. Vingron. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics, 1:1:9, 2002.
12. R. Irizarry, F. C. B. Hobbs, Y. Beaxer-Barclay, K. Antonellis. U. Scherf, and T. Speed. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistica, 4:249-264, 2003.
13. R. Irizarry, L. Gautier, and L. Cope. An R package for analyses of affymetrix oligonucleotide arrays. In G. Parmigiani, E. Garrett, R. Irizarry. and S. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software. Springer, Berlin, 2003.
14. R. A. Irizarry, B. M. Bolstad, F. Collin, L. M. Cope, B. Hobbs, and T. P. Speed. Summaries of affymetrix genechip probe level data. Nucleic Acids Research, 31, 2003.
15. M. K. Kerr, M. Martin, and G. A. Churchill. Analysis of variance for gene expression microarray data. Journal of Computational Biology, 7:819-837, 2000.
16. C. Li and W. Wong. Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proceedings of the National Academy of Science U S A, 98:31-36, 2001.
17. F. Naef, D. A. Lim, N. Patil, and M. O. Magnasco. From features to expression: High density oligonucleotide array analysis revisited. Tech Report, 1:1-9, 2001.
18. F. Naef and M. O. Magnasco. Solving the riddle of the bright mismatches: labeling and effective binding in oligonucleotide arrays. Physical Review E, 68:011906, 2003.
19. M. Newton, C. Kendziorski, C. Richmond, and F. Blattner. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology, 8:37:52, 2001.
20. R. Wolfinger, G. Gibson, E. Wolfinger, L. Bennett, H. Hamadeh, P. Bushel, C. Afshari, and R. Paules. Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology, 8(6):625-637, 2001.
21. C. Workman, L. J. Jensen, H. Jarmer, R. Berka, L. Gautier, H. B. Nielsen, H. Saxild, C. Nielsen, S. Brunak, and S. Knudsen. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biology, 3, 2002.
22. Z. Wu, R. A. Irizarry, R. Gentleman, F. M. Murillo, and F. Spencer. A model based background adjustment for oligonucleotide expression arrays. Technical report, Johns Hopkins University, Dept. of Biostatistics Working Papers, 2003. http://www.bepress.com/jhubiostat/paper1.
23. L. Zhang, M. F. Miles, and K. D. Aldape. A model of molecular interactions on short oligonucleotide microarrays: implications for probe design and data analysis. Nature Biotechnology, 21:818-821, 2003.
24. L. Zhang, L. Wang, A. Ravindranathan. and M. Miles. A new algorithm for analysis of oligonucleotide arrays: application to expression profiling in mouse brain regions. J Mol Bio, 317:227-235, 2002.
25. G. Zipf. Human Behaviour and the principle of least effort. Addison-Wesley, Cambridge, MA, 1949.