Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models

BMC Bioinformatics

Volumn 6, Issue , 2005, Pages

  Hu, Pingzhao ^a   Greenwood, Celia M T ^a,b   Beyene, Joseph ^a,b  

^a HOSPITAL FOR SICK CHILDREN RESEARCH INSTITUTE   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODOLOGY; DIFFERENTIALLY EXPRESSED GENE; GENE EXPRESSION DATA; GENE EXPRESSION PROFILES; INTEGRATIVE ANALYSIS; MICROARRAY DATA SETS; SYSTEMATIC INTEGRATION; WEIGHTING STRATEGIES;

GENE EXPRESSION; OLIGONUCLEOTIDES;

DATA INTEGRATION;

COMPLEMENTARY DNA; OLIGONUCLEOTIDE;

ANALYTIC METHOD; ARTICLE; CONTROLLED STUDY; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC ANALYSIS; GENETIC IDENTIFICATION; GENETIC MODEL; GENETIC VARIABILITY; HUMAN; OLIGONUCLEOTIDE PROBE; QUALITY CONTROL; STATISTICAL ANALYSIS; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BIOLOGY; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENETIC DATABASE; LUNG TUMOR; METABOLISM; PROCEDURES; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DATABASES, GENETIC; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; LUNG NEOPLASMS; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; QUALITY CONTROL;

EID: 25444521278     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-6-128     Document Type: Article

References (36)

1. Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang-odriguez J, Moskaluk CA, Frierson HF, Hampton GM: Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Research 2001, 61:5974-5978.
2. Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer. Nature 2001, 412:822-826.
3. Luo J, Duggan DJ, Chen Y, Sauvageot J, Ewing CM, Bittner ML, Trent JM, Issacs WB: Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Research 2001, 61:4683-4688.
4. Magee JA, Araki T, Patil S, Ehrig T, True L, Humphrey PA, Catalona WJ, Watson MA, Milbrandt J: Expression profiling reveals hepsin overexpression in prostate cancer. Cancer Research 2001, 61:5692-5696.
5. Olkin I: Meta-Analysis: methods for combining independent studies. Editors introduction. Statistical Science 1992, 7:226.
6. Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM: Meta-analysis of microarrays: inter-study validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Research 2002, 62:4427-4433.
7. Choi JK, Yu U, Kim S, Yoo OJ: Combining multiple microarray studies and modeling interstudy variation. Bioinformatics 2003, Suppl.19: i84-i90.
8. Ghosh D, Barette TR, Rhodes D, Chinnaiyan AM: Statistical issues and methods for metaanalysis of microarray data: a case study in prostate cancer. Functional & Integrative Genomics 2003, 3:180-188.
9. Parmigiani G, Garrett-Mayer ES, Anbazhagan R, Gabrielson E: A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clinical Cancer Research 2004, 10:2922-2927.
10. Jiang H, Deng Y, Chen H, Tao L, Sha Q, Chen J, Tsai C, Zhang S: Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics 2004, 5:81.
11. Shen R, Ghosh D, Chinnaiyan AM: Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data. BMC Genomics 2004, 5:94
12. Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 2002, 18:405-412.
13. Jarvinen AK, Hautaniemi S, Edgren H, Auvinen P, Saarela J, Kallioniemi OP, Monni O: Are data from different gene expression microarray platforms comparable? Genomics 2004, 83:1164-1168.
14. Tritchler D: Modelling study quality in meta-analysis. Statistics in Medicine 1999, 18:2135-2145.
15. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG, Lizyness ML, Kuick R, Hayasaka S, Taylor JM, Iannettoni MD, Orringer MB, Hanash S: Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Medicine 2002, 9:816-824.
16. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, Loda M, Weber G, Mark EJ, Lander ES, Wong W, Johnson BE, Golub TR, Sugarbaker DJ, Meyerson M: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences USA 2001, 98:13790-13795.
17. Walker JKL, Gainetdinov RR, Feldman DS, McFawn PK, Caron MG, Lefkowitz RJ, Premount RT, Fisher JT: G protein-coupled receptor kinase 5 regulates airway response induced by muscarinic receptor activation. American Journal of Physiology - Lung Cell Molecular Physiology 2004, 286:L312-L319.
18. CAMDA 2003 [http://www.camda.duke.edu/camda03]
19. Shigematsu H, Lin L, Takahashi T, Nomura M, Suzuki M, Wistuba II, Fong KM, Lee H, Toyooka S, Shimizu N, Fujisawa T, Feng Z, Roth JA, Herz J, Minna JD, Gazdar AF: Clinical and biological features associated with epidermal growth factor receptor gene mutations in lung cancers. Journal of National Cancer Institute 2005, 97:339-346.
20. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research 2003, 31:1-8.
21. Brigham HM, Gregory TK, Jeffrey S, Meena A, David B, Peter B, Daniel ZW, Thomas JM, Isaac SK, Zoltan S: Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Research 2004, 32:1:8
22. Affymetrix Microarray Suite User Guide, version 5; 2001 [http://www.affymetrix.com/support/technical/manuals.affx.]
23. Allison DB, Gadbury GL, Heo M, Fernandez JR, Lee CK, Prolla TA, Weindruch R: A mixture model approach for the analysis of microarray gene expression data. Computational Statistics & Data Analysis 2002, 39:1-20
24. Pounds S, Morris SW: Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 2003, 19:1236-1242.
25. Knight K: Mathematical statistics. Chapman & Hall/CRC Press; 2000.
26. Hedges LV, Olkin I: Statistical methods for meta-analysis. Orlando, FL: Academic Press pp81;1995.
27. Cooper H, Hedges LV: The handbook of research synthesis. New York: Russell Sage, pp. 268;1994.
28. Cochran BG: The combination of estimates from different experiments. Biometrics 1954, 10: 101-129.
29. DerSimonian R, Laird NM: Meta-analysis in clinical trials. Controlled Clinical Trials 1986, 7:177-188.
30. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences USA 2001, 98:5116-5121.
31. Efron B, Tibshirani R, Tusher V: Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association 2001, 96:1151-1160.
32. Broberg P: Statistical methods for ranking differentially expressed genes. Genome Biology 2003, 4: R41
33. Smyth GK: Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology 2004: Vol. 3: No. 1, Article 3.
34. Garrett-Mayer E, Parmigiani G, Zhong X, Cope L, Gabrielson E: Cross-study Validation and Combined Analysis of Gene Expression Microarray Data. Technical Report, Johns Hopkins University, Department of Biostatistics; 2004. [http://www.bepress.com/jhubiostat/paper65]
35. Dudoit S, Shaffer JP, Boldrick JC: Multiple hypothesis testing in microarray experiments. Statistical Science 2003, 18:71-103.
36. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B 1995, 85:289-300.