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Original Articles

Hierarchical Bayesian meta-analysis models for cross-platform microarray studies

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Pages 1067-1085 | Received 23 Feb 2008, Published online: 24 Sep 2009

References

  • Baldi , P. and Long , A. D. 2001 . A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes . Bioinformatics , 17 : 509 – 519 .
  • Benjamini , Y. and Hochberg , Y. 1995 . Controlling the false discovery rate: a practical and powerful approach to multiple testing . J. R. Statist. Soc. B , 85 : 289 – 300 .
  • Bhowmick , D. , Davison , A. C. , Goldstein , D. R. and Ruffieux , Y. 2006 . A Laplace mixture model for identification of differential expression in microarray experiments . Biostatistics , 7 : 630 – 641 .
  • Brazma , A. , Parkinson , H. , Sarkans , U. , Shojatalab , M. , Vilo , J. , Abeygunawardena , N. , Holloway , E. , Kapushesky , M. , Kemmeren , P. , Lara , G. G. , Oezcimen , A. , Rocca-Serra , P. and Sansone , S. A. 2003 . ArrayExpress – a public repository for microarray gene expression data at the EBI . Nucleic Acids Res. , 31 : 68 – 71 .
  • Broët , P. , Richardson , S. and Radvanyi , F. 2002 . Bayesian hierarchical model for identifying changes in gene expression from microarray experiments . J. Comput. Biol. , 9 : 671 – 683 .
  • Cahan , P. , Rovegno , F. , Mooney , D. , Newman , J. C. , St. Laurent , G. 3rd and McCaffrey , T. A. 2007 . Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization . Gene , 401 : 12 – 18 .
  • Choi , J. K. , Yu , U. , Kim , S. and Yoo , O. J. 2003 . Combining multiple microarray studies and modeling inter-study variation . Bioinformatics , 19 ( Suppl. 1 ) : i84 – i90 .
  • Conlon , E. M. , Song , J. J. and Liu , J. S. 2006 . Bayesian models for pooling microarray studies with multiple sources of replications . BMC Bioinformatics , 7 : 247
  • Conlon , E. M. , Song , J. J. and Liu , A. 2007 . Bayesian meta-analysis models for microarray data: a comparative study . BMC Bioinformatics , 8 : 80
  • Do , K. A. , Müller , P. and Tang , F. 2005 . A Bayesian mixture model for differential gene expression . J. R. Statist. Soc. C , 54 : 627 – 644 .
  • Edgar , R. , Domrachev , M. and Lash , A. E. 2002 . Gene expression omnibus: NCBI gene expression and hybridization array data repository . Nucleic Acids Res. , 30 : 207 – 210 .
  • Efron , B. , Tibshirani , R. , Storey , J. D. and Tusher , V. G. 2001 . Empirical Bayes analysis of a microarray experiment . J. Am. Statist. Assoc. , 96 : 1151 – 1160 .
  • Genovese , C. and Wasserman , L. 2002 . Operating characteristics and extensions of the false discovery rate procedure . J. R. Statist. Soc. B , 64 : 499 – 518 .
  • Genovese , C. and Wasserman , L. 2003 . “ Bayesian and frequentist multiple testing ” . In Bayesian Statistics , Edited by: Bernardo , J. M. Vol. 7 , 145 – 162 . Oxford : Oxford University Press .
  • Ghosh , D. , Barette , T. R. , Rhodes , D. and Chinnaiyan , A. M. 2003 . Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer . Funct. Integr. Genomics , 3 : 180 – 188 .
  • Gottardo , R. , Pannucci , J. A. , Kuske , C. R. and Brettin , T. 2003 . Statistical analysis of microarray data: a Bayesian approach . Biostatistics , 4 : 597 – 620 .
  • Hedges , L. V. and Olkin , I. 1985 . Statistical Methods for Meta-analysis , Orlando : Academic Press .
  • Hu , P. , Greenwood , C. M.T. and Beyene , J. 2005 . Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models . BMC Bioinformatics , 6 : 128
  • Ibrahim , J. G. , Chen , M. H. and Gray , R. J. 2002 . Bayesian models for gene expression with DNA microarray data . J. Am. Statist. Assoc. , 97 : 88 – 99 .
  • Ishwaran , H. and Rao , J. S. 2003 . Detecting differentially expressed genes in microarrays using Bayesian model selection . J. Am. Statist. Assoc. , 98 : 438 – 455 .
  • Ishwaran , H. and Rao , J. S. 2005 . Spike and slab gene selection for multigroup microarray data . J. Am. Statist. Assoc. , 100 : 764 – 780 .
  • Jiang , H. , Deng , Y. , Chen , H. , Tao , L. , Sha , Q. , Chen , J. , Tsai , C. and Zhang , S. 2004 . Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes . BMC Bioinformatics , 5 : 81
  • Jung , Y. Y. , Oh , M. S. , Shin , D. W. , Kang , S. H. and Oh , H. S. 2006 . Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering . Biom. J. , 48 : 435 – 450 .
  • Kendziorski , C. M. , Newton , M. A. , Lan , H. and Gould , M. N. 2003 . On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles . Stat. Med. , 22 : 3899 – 3914 .
  • Liu , J. S. 2001 . Monte Carlo Strategies in Scientific Computing , New York : Springer-Verlag .
  • Liu , R. H. , Dill , K. , Fuji , H. S. and McShea , A. 2006a . Integrated microfluidic biochips for DNA microarray analysis . Expert Rev. Mol. Diagn. , 6 : 253 – 261 .
  • Liu , R. H. , Nguyen , T. , Schwarzkopf , K. , Fuji , H. S. , Petrova , A. , Siuda , T. , Peyvan , K. , Bizak , M. , Danley , D. and McShea , A. 2006b . Fully integrated miniature device for automated gene expression DNA microarray processing . Anal. Chem. , 78 : 1980 – 1986 .
  • Lönnstedt , I. and Britton , T. 2005 . Hierarchical Bayes models for cDNA microarray gene expression . Biostatistics , 6 : 279 – 291 .
  • Lönnstedt , I. and Speed , T. P. 2002 . Replicated microarray data . Stat. Sin. , 12 : 31 – 46 .
  • Methé , B. A. , Nelson , K. E. , Eisen , J. A. , Paulsen , I. T. , Nelson , W. , Heidelberg , J. F. , Wu , D. , Wu , M. , Ward , N. , Beanan , M. J. , Dodson , R. J. , Madupu , R. , Brinkac , L. M. , Daugherty , S. C. , DeBoy , R. T. , Durkin , A. S. , Gwinn , M. , Kolonay , J. F. , Sullivan , S. A. , Haft , D. H. , Selengut , J. , Davidsen , T. M. , Zafar , N. , White , O. , Tran , B. , Romero , C. , Forberger , H. A. , Weidman , J. , Khouri , H. , Feldblyum , T. V. , Utterback , T. R. , Van Aken , S. E. , Lovley , D. R. and Fraser , C. M. 2003 . Genome of Geobacter sulfurreducens: metal reduction in subsurface environments . Science , 302 : 1967 – 1969 .
  • Methé , B. A. , Webster , J. , Nevin , K. , Butler , J. and Lovley , D. R. 2005 . DNA microarray analysis of nitrogen fixation and Fe(III) reduction in Geobacter sulfurreducens . Appl. Environ. Microbiol. , 71 : 2530 – 2538 .
  • Morris , J. S. , Yin , G. , Baggerly , K. A. , Wu , C. and Zhang , L. 2005 . “ Pooling information across different studies and oligonucleotide microarray chip types to identify prognostic genes for lung cancer ” . In Methods of Microarray Data Analysis , Edited by: Shoemaker , J. S. and Lin , S. M. Vol. IV , 51 – 66 . New York : Springer-Verlag .
  • Newton , M. A. , Kendziorski , C. M. , Richmond , C. S. , Blattner , F. R. and Tsui , K. W. 2001 . On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data . J. Comput. Biol. , 8 : 37 – 52 .
  • Newton , M. A. , Noueiry , A. , Sarkar , D. and Ahlquist , P. 2004 . Detecting differential gene expression with a semiparametric hierarchical mixture method . Biostatistics , 5 : 155 – 176 .
  • Park , T. , Yi , S. G. , Shin , Y. K. and Lee , S. 2006 . Combining multiple microarrays in the presence of controlling variables . Bioinformatics , 22 : 1682 – 1689 .
  • Parmigiani , G. , Garrett-Mayer , E. S. , Anbazhagan , R. and Gabrielson , E. 2002 . A statistical framework for expression-based molecular classification in cancer (with discussion) . J. R. Statist. Soc. B , 64 : 717 – 736 .
  • Parmigiani , G. , Garrett-Mayer , E. S. , Anbazhagan , R. and Gabrielson , E. 2004 . A cross-study comparison of gene expression studies for the molecular classification of lung cancer . Clin. Cancer Res. , 10 : 2922 – 2927 .
  • Postier , B. , DiDonato , R. , Nevin , K. , Liu , A. , Frank , B. , Lovley , D. and Methé , B. A. 2007 . Benefits of in-situ synthesized microarrays for analysis of gene expression in understudied microorganisms . J. Microbiol. Meth. , 74 : 26 – 32 .
  • Rhodes , D. R. , Barrette , T. R. , Rubin , M. A. , Ghosh , D. and Chinnaiyan , A. M. 2002 . Meta-analysis of microarrays: inter-study validation of gene expression profiles reveals pathway dysregulation in prostate cancer . Cancer Res. , 62 : 4427 – 4433 .
  • Rhodes , D. R. , Yu , J. , Shanker , K. , Deshpande , N. , Varambally , R. , Ghosh , D. , Barrette , T. , Pandey , A. and Chinnaiyan , A. M. 2004 . Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression . Proc. Natl Acad. Sci. USA , 101 : 9309 – 9314 .
  • Saeed , A. I. , Sharov , V. , White , J. , Li , J. , Liang , W. , Bhagabati , N. , Braisted , J. , Klapa , M. , Currier , T. , Thiagarajan , M. , Sturn , A. , Snuffin , M. , Rezantsev , A. , Popov , D. , Ryltsov , A. , Kostukovich , E. , Borisovsky , I. , Liu , Z. , Vinsavich , A. , Trush , V. and Quackenbush , J. 2003 . TM4: a free, open-source system for microarray data management and analysis . Biotechniques , 34 : 374 – 378 .
  • Sebastiani , P. , Xie , H. and Ramoni , M. F. 2006 . Bayesian analysis of comparative microarray experiments by model averaging . Bayesian Anal. , 1 : 707 – 732 .
  • Shen , R. , Ghosh , D. and Chinnaiyan , A. M. 2004 . Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data . BMC Genom. , 5 : 94
  • Spiegelhalter , D. J. , Best , N. G. , Carlin , B. R. and van der Linde , A. 2002 . Bayesian measures of model complexity and fit (with discussion) . J. R. Statist. Soc. B , 64 : 583 – 639 .
  • Spiegelhalter , D. J. , Thomas , A. and Best , N. G. 2003 . WinBUGS Version 1.4 User Manual , Cambridge : Medical Research Council Biostatistics Unit .
  • Stangl , D. K. and Berry , D. A. 2000 . “ Meta-analysis: past and present challenges ” . In Meta-analysis in Medicine and Health Policy , Edited by: Stangl , D. K. and Berry , D. A. 1 – 28 . New York : Marcel Dekker .
  • Stevens , J. R. and Doerge , R. W. 2005 . Combining affymetrix microarray results . BMC Bioinformatics , 6 : 57
  • Storey , J. D. 2002 . A direct approach to false discovery rates . J. R. Statist. Soc. B , 64 : 479 – 498 .
  • Storey , J. D. and Tibshirani , R. 2003 . “ SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays ” . In The Analysis of Gene Expression Data: Methods and Software , Edited by: Parmigiani , G. 272 – 290 . New York : Springer .
  • Townsend , J. P. and Hartl , D. L. 2002 . “ Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple treatments or samples ” . In Genome Biol. Vol. 3 , research0071.1-71.16
  • Tseng , G. C. , Oh , M. K. , Rohlin , L. , Liao , J. C. and Wong , W. H. 2001 . Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects . Nucleic Acids Res. , 29 : 2549 – 2557 .
  • Tusher , V. G. , Tibshirani , R. and Chu , G. 2001 . Significance analysis of microarrays applied to the ionizing radiation response . Proc. Natl Acad. Sci. USA , 98 : 5116 – 5121 .
  • Tweedie , R. L. , Scott , D. J. , Biggerstaff , B. J. and Mengersen , K. L. 1996 . Bayesian meta-analysis, with application to studies of ETS and lung cancer . Lung Cancer , 14 ( Suppl 1 ) : S171 – S194 .
  • Wang , J. , Coombes , K. R. , Highsmith , W. E. , Keating , M. J. and Abruzzo , L. V. 2004 . Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies . Bioinformatics , 20 : 3166 – 3178 .
  • Warnat , P. , Eils , R. and Brors , B. 2005 . Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes . BMC Bioinformatics , 6 : 265
  • Xiao , G. , Martinez-Vaz , B. , Pan , W. and Khodursky , A. B. 2006 . Operon information improves gene expression estimation for cDNA microarrays . BMC Genom. , 7 : 87
  • Xu , L. , Tan , A. C. , Naiman , D. Q. , Geman , D. and Winslow , R. L. 2005 . Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data . Bioinformatics , 21 : 3905 – 3911 .

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