Biostatistics (2004), 5, 2, pp. 155-176
Biostatistics Vol. 5 No. 2 © Oxford University Press 2004; all rights reserved.
Detecting differential gene expression with a semiparametric hierarchical mixture method
Department of Statistics, University of Wisconsin–Madison, 1210 West Dayton St., Madison, WI 53706-1685, USA and Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, 600 Highland Ave., Madison, WI 53792, USA
newton{at}stat.wisc.edu
Institute for Molecular Virology, University of Wisconsin–Madison,1525 Linden Drive Madison, WI 53706, USA
Department of Statistics, University of Wisconsin–Madison, 1210 West Dayton St., Madison, WI 53706-1685, USA
Institute for Molecular Virology, University of Wisconsin–Madison,1525 Linden Drive Madison, WI 53706, USA and Howard Hughes Medical Institute, USA
* To whom correspondence should be addressed.
Mixture modeling provides an effective approach to the differential expression problem in microarray data analysis. Methods based on fully parametric mixture models are available, but lack of fit in some examples indicates that more flexible models may be beneficial. Existing, more flexible, mixture models work at the level of one-dimensional gene-specific summary statistics, and so when there are relatively few measurements per gene these methods may not provide sensitive detectors of differential expression. We propose a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data. EM-based algorithms are used to fit both parametric and semiparametric versions of the model. We restrict attention to the two-sample comparison problem; an experiment involving Affymetrix microarrays and yeast translation provides the motivating case study. Gene-specific posterior probabilities of differential expression form the basis of statistical inference; they define short gene lists and false discovery rates. Compared to several competing methodologies, the proposed methodology exhibits good operating characteristics in a simulation study, on the analysis of spike-in data, and in a cross-validation calculation.
Keywords: Empirical Bayes; False discovery rate; Microarrays; Ordered alternatives; Posterior probability; Statistical analysis
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S. Lu, J. Li, C. Song, K. Shen, and G. C. Tseng Biomarker detection in the integration of multiple multi-class genomic studies Bioinformatics, February 1, 2010; 26(3): 333 - 340. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 H. Noma, S. Matsui, T. Omori, and T. Sato Bayesian ranking and selection methods using hierarchical mixture models in microarray studies Biostat., November 27, 2009; (2009) kxp047v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. M. Schlatzer, J.-E. Dazard, M. Dharsee, R. M. Ewing, S. Ilchenko, I. Stewart, G. Christ, and M. R. Chance Urinary Protein Profiles in a Rat Model for Diabetic Complications Mol. Cell. Proteomics, September 1, 2009; 8(9): 2145 - 2158. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. T. Leek and J. D. Storey A general framework for multiple testing dependence PNAS, December 2, 2008; 105(48): 18718 - 18723. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 X. D. Zhang, P. F. Kuan, M. Ferrer, X. Shu, Y. C. Liu, A. T. Gates, P. Kunapuli, E. M. Stec, M. Xu, S. D. Marine, et al. Hit selection with false discovery rate control in genome-scale RNAi screens Nucleic Acids Res., August 1, 2008; 36(14): 4667 - 4679. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. T. Flowers, M. P. Keller, Y. Choi, H. Lan, C. Kendziorski, J. M. Ntambi, and A. D. Attie Liver gene expression analysis reveals endoplasmic reticulum stress and metabolic dysfunction in SCD1-deficient mice fed a very low-fat diet Physiol Genomics, May 1, 2008; 33(3): 361 - 372. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Ji, Y. Lu, and G. B. Mills Bayesian models based on test statistics for multiple hypothesis testing problems Bioinformatics, April 1, 2008; 24(7): 943 - 949. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. Hong and R. Breitling A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments Bioinformatics, February 1, 2008; 24(3): 374 - 382. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Shen, Z. Wang, G. Shankar, X. Zhang, and L. Li A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry Bioinformatics, January 15, 2008; 24(2): 202 - 208. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Chen and C. Kendziorski A Statistical Framework for Expression Quantitative Trait Loci Mapping Genetics, October 1, 2007; 177(2): 761 - 771. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Jia and S. Xu Mapping Quantitative Trait Loci for Expression Abundance Genetics, May 1, 2007; 176(1): 611 - 623. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Anisimova and Z. Yang Multiple Hypothesis Testing to Detect Lineages under Positive Selection that Affects Only a Few Sites Mol. Biol. Evol., May 1, 2007; 24(5): 1219 - 1228. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. D. Storey, J. Y. Dai, and J. T. Leek The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments Biostat., April 1, 2007; 8(2): 414 - 432. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Lo and R. Gottardo Flexible empirical Bayes models for differential gene expression Bioinformatics, February 1, 2007; 23(3): 328 - 335. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. S. Yuan and R. A. Irizarry High-resolution spatial normalization for microarrays containing embedded technical replicates Bioinformatics, December 15, 2006; 22(24): 3054 - 3060. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. S. Mehta, S. O. Zakharkin, G. L. Gadbury, and D. B. Allison Epistemological issues in omics and high-dimensional biology: give the people what they want Physiol Genomics, December 13, 2006; 28(1): 24 - 32. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G.J. McLachlan, R.W. Bean, and L. B.-T. Jones A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays Bioinformatics, July 1, 2006; 22(13): 1608 - 1615. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Guindon, M. Black, and A. Rodrigo Control of the False Discovery Rate Applied to the Detection of Positively Selected Amino Acid Sites Mol. Biol. Evol., May 1, 2006; 23(5): 919 - 926. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Broet and S. Richardson Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model Bioinformatics, April 15, 2006; 22(8): 911 - 918. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Ploner, S. Calza, A. Gusnanto, and Y. Pawitan Multidimensional local false discovery rate for microarray studies Bioinformatics, March 1, 2006; 22(5): 556 - 565. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. A. van de Wiel, J. L. Costa, K. Smid, C. B.M. Oudejans, A. M. Bergman, G. A. Meijer, G. J. Peters, and B. Ylstra Expression Microarray Analysis and Oligo Array Comparative Genomic Hybridization of Acquired Gemcitabine Resistance in Mouse Colon Reveals Selection for Chromosomal Aberrations Cancer Res., November 15, 2005; 65(22): 10208 - 10213. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Ji and W. H. Wong TileMap: create chromosomal map of tiling array hybridizations Bioinformatics, September 15, 2005; 21(18): 3629 - 3636. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Kalajzic, A. Staal, W.-P. Yang, Y. Wu, S. E. Johnson, J. H. M. Feyen, W. Krueger, P. Maye, F. Yu, Y. Zhao, et al. Expression Profile of Osteoblast Lineage at Defined Stages of Differentiation J. Biol. Chem., July 1, 2005; 280(26): 24618 - 24626. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. T. Barry, A. B. Nobel, and F. A. Wright Significance analysis of functional categories in gene expression studies: a structured permutation approach Bioinformatics, May 1, 2005; 21(9): 1943 - 1949. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. K. Smyth, J. Michaud, and H. S. Scott Use of within-array replicate spots for assessing differential expression in microarray experiments Bioinformatics, May 1, 2005; 21(9): 2067 - 2075. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. H. Yang, Y. Xiao, and M. R. Segal Identifying differentially expressed genes from microarray experiments via statistic synthesis Bioinformatics, April 1, 2005; 21(7): 1084 - 1093. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Gannoun, J. Saracco, W. Urfer, and G. E. Bonney Nonparametric analysis of replicated microarray experiments Statistical Modeling, October 1, 2004; 4(3): 195 - 209. [Abstract] [PDF]
|
|