Incorporating prior information via shrinkage: A combined analysis of genome-wide location data and gene expression data

Statistics in Medicine

Volumn 26, Issue 10, 2007, Pages 2258-2275

  Xie, Yang ^a   Pan, Wei ^a,c   Jeong, Kyeong S ^b   Khodursky, Arkady ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c Mayo   (United States)

Author keywords

Integrative analysis; Microarray; Permutation; Statistical power

Indexed keywords

LEUCINE RESPONSIVE REGULATORY PROTEIN; TRANSCRIPTION FACTOR;

ACCURACY; ANALYTIC METHOD; ARTICLE; BAYES THEOREM; BINDING SITE; CONTROLLED STUDY; DATA ANALYSIS; ESCHERICHIA COLI; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; GENE TARGETING; GENETIC CODE; GENETIC SCREENING; GENOME ANALYSIS; INTERMETHOD COMPARISON; MICROARRAY ANALYSIS; NONHUMAN; SENSITIVITY ANALYSIS; SIMULATION; TRANSCRIPTION REGULATION;

BAYES THEOREM; DATA INTERPRETATION, STATISTICAL; ESCHERICHIA COLI; GENE EXPRESSION; MICROARRAY ANALYSIS; MODELS, STATISTICAL; ROC CURVE; UNITED STATES;

EID: 34247590196     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2703     Document Type: Article

References (31)

1. Ren B, Robert F et al. Genome-wide location and function of DNA binding proteins. Science 2000; 290: 2306-2309.
2. Iyer V, Horak C, Scafe C, Bostein D, Snyder M, Brown P. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 2001; 409:533-538.
3. Zheng M, Barrera LO, Ren B, Wu YN. ChIP-chip: data, model and analysis. UCLA Statistics Preprint 443, 2005. Available at http://preprints.stat.ucla.edu
4. Shannon MF, Rao S. Transcription: Of chips and ChIPs. Science 2002; 296:666-669.
5. Simon I et al. Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 2001; 106:697-708.
6. Buck M, Lieb J. ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 2004; 83:349-360.
7. Tibshirani R, Hastie R, Narasimhan B, Chu G. Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statistical Science 2003; 18:104-117.
8. Huang X, Pan W. Linear regression and two-class classification with gene expression data. Bioinformatics 2003; 19:2072-2078.
9. Carlin BP, Louis TA. Bayes and Empirical Bayes Methods for Data Analysis. Chapman & Hall/CRC Press: London, Boca Raton, FL, 2000.
10. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer: Berlin, 2001.
11. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences 2001; 98:5116-5121.
12. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001; 17:509-519.
13. Efron B, Tibshirani R, Storey JD, Tusher V. Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association 2001; 96:1151-1160.
14. Wright GW, Simon RM. A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 2003; 19:2448-2455.
15. Pan W, Lin J, Le C. A mixture model approach to detecting differentially expressed genes with microarray data. Functional and Integrative Genomics 2003; 3:117-124.
16. Xu XL, Olsen JM, Zhao LP. A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model. Human Molecular Genetics 2002; 11:1977-1985.
17. Pan W. On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Bioinformatics 2003; 19:1333-1340.
18. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. JRSS-B 1995; 57:289-300.
19. Storey JD, Tibshirani R. Statistical significance for genome-wide experiments. Proceedings of the National Academy of Sciences of the United States of America 2003; 100:9440-9445.
20. Xie Y, Pan W, Khodursky AB. A note on using permutation based false discovery rate estimates to compare different analysis methods for microarray data. Bioinformatics 2005; 21:4280-4288.
21. Fan JQ, Li RZ. Variable selection via nonconcave penalized likelihood and its oracle properties. JASA 2001; 96:1348-1360.
22. Tibshirani R. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, Series B 1996; 58:267-288.
23. Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994; 81:425-455.
24. Donoho DL. De-noising by soft-thresholding. IEEE Transactions on Information Theory 1995; 41:613-627.
25. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B 2005; 67:301-320.
26. Tani T, Khodursky A, Blumenthal R, Brown P, Matthews R. Adaptation to famine: a family of stationary-phase genes revealed by microarray analysis. Proceedings of the National Academic of Sciences of the United States of America 2002; 99:13471-13476.
27. Yang YH, Dudoit S, Luu P, Speed T. Normalization for cDNA microarray data. Nucleic Acids Research 2002; 30:e15.
28. Zhi J, Mathew E, Freundlich M. Lrp binds to two regions in the dadAX promoter region of Escherichia coli to repress and activate transcription directly. Molecular Microbiology 1999; 32:29-40.
29. Mathew E, Zhi J, Freundlich M. Lrp is a direct repressor of the dad operon in Escherichia coli. Journal of Bacteriology 1996; 178:7234-7240.
30. Broet P, Lewin A, Richardson S, Dalmasso C, Magdelenat H. A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments. Bioinformatics 2004; 20:2562-2571.
31. Cui X, Hwang JTG, Qiu J, Blades NJ, Churchill GA. Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 2005; 6:59-75.