A two-stage empirical Bayes method for identifying differentially expressed genes

Computational Statistics and Data Analysis

Volumn 50, Issue 12, 2006, Pages 3592-3604

  Ji, Yuan ^a   Tsui, Kam Wah ^b   Kim, KyungMann ^c  

^a UNIVERSITY OF TEXAS   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

Author keywords

Abundance group; Conjugate priors; False discovery rate; Microarrays; Moment matching; Toxicogenomics

Indexed keywords

CALIBRATION; COMPUTATION THEORY; DATA REDUCTION; PROBABILITY;

ABUNDANCE GROUP; CONJUGATE PRIORS; FALSE DISCOVERY RATE; MICROARRAYS; MOMENT MATCHING; TOXICOGENOMICS;

GENES;

EID: 33646929817     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2005.07.020     Document Type: Article

References (18)

1. Baggerly K.A., Coombes K.R., Hess K.R., Stivers D.N., Abruzzo L.V., and Zhang W. Identifying differentially expressed genes in cDNA microarray experiments. J. Comput. Biol. 8 (2001) 639-659
2. Benjamini Y., and Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. B 57 (1995) 289-300
3. Chen Y., Dougherty E., and Bittner M. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J. Biomed. Opt. 2 (1997) 364-367
4. Dudoit S., Yang Y., Callow M., and Speed T. Statistical methods for identifying differentially expressed genes in replicated cdna microarray experiments. Statist. Sin. 12 (2002) 111-141
5. Efron B., Tibshirani R., Storey J., and Tusher V. Empirical bayes analysis of a microarray experiment. J. Amer. Statist. Assoc. 96 (2001) 1151-1160
6. Fraley C., and Raftery A. MCLUST: software for model-based cluster analysis. J. Classification 16 (1999) 297-306
7. Ibrahim J., Chen M.-H., and Gray R. Bayesian models for gene expression with DNA microarray data. J. Amer. Statist. Assoc. 97 (2002) 88-100
8. Irizarry R., Bolstad B., Collin F., Cope L., Hobbs B., and Speed T. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31 (2003) e15
9. Ji Y., Tsui K., and Kim K. A novel means of using gene clusters in a two-step empirical bayes method for prediciting classes of samples. Bioinformatics 21 (2005) 1055-1061
10. Kendziorski C., Newton M., Lan H., and Gould M. On parametric empirical bayes methods for comparing multiple groups using replicated gene expression profiles. Statist. Medicine 22 (2003) 3899-3914
11. Kerr K., Afshari C., Bennett L., Bushel P., Martinez J., Walker N., and Churchill G. Statistical analysis of a gene expression microarray experiment with replication. Statist. Sin. 12 (2002) 203-218
12. Newton M., Kendziorski C., Richmond C., Blattner F., and Tsui K. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J. Comput. Biol. 8 (2001) 37-52
13. Newton M., Noueiry A., Sarkar D., and Ahlquist P. Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 (2004) 155-176
14. Pan, W., Lin, J., Le, C., 2001. A mixture model approach to detecting differentially expressed genes with microarray data. Technical Report 2001-011, Division of Biostatistics, University of Minnesota.
15. Schwarz G. Estimating the dimension of a model. Ann. Statist. 6 (1978) 461-464
16. Tadesse M., Ibrahim J., and Mutter G. Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology. Biometrics 59 (2003) 542-554
17. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Nat. Acad. Sci. 98 (2001) 5116-5121
18. Zhang L., Miles M., and Aldape K. A model of molecular interactions on short oligonucleotide microarrays. Nature Biotechnol. 21 (2003) 818-821