iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data

Abstract

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model. Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma. Availability: http://odin.mdacc.tmc.edu/∼vbaladan/. Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

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References 47

1. 1.

   Albert JH, Chib S. Bayesian analysis of binary and polychotomous response data. J. Am. Stat. Assoc. 1993;88:669-679.

   Not in our library

2. 2.

   Aronica E, et al. Overexpression of the human major vault protein in gangliogliomas. Epilepsia. 2003;44:1166-1175.

   Not in our library10.1046/j.1528-1157.2003.22103.x (opens in a new tab)

3. 3.

   Bass AJ, et al. SOX2 is an amplified lineage-survival oncogene in lung and esophageal squamous cell carcinomas. Nat. Genet. 2009;41:1238-1242.

   Not in our library10.1038/ng.465 (opens in a new tab)

4. 4.

   Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B. 1995;57:289-300.

   Not in our library

5. 5.

   Bonato V, et al. Bayesian ensemble methods for survival prediction in gene expression data. Bioinformatics. 2011;27:359-367.

   Not in our library10.1093/bioinformatics/btq660 (opens in a new tab)

6. 6.

   Casella G, George EI. Explaining the Gibbs sampler. Am. Stat. 1992;46:167-174.

   Not in our library

7. 7.

   Chin L, et al. Making sense of cancer genomic data. Genes Dev. 2011;25:534-555.

   Not in our library10.1101/gad.2017311 (opens in a new tab)

8. 8.

   Daemen A, et al. A kernel-based integration of genome-wide data for clinical decision support. Genome Med. 2009;1:39.

   Not in our library10.1186/gm39 (opens in a new tab)

9. 9.

   Dennis G, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4:P3.

   Not in our library

10. 10.
    Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach · 2009

    Not checked
11. 11.

    Efron B, et al. Least angle regression. Ann. Statist. 2004;32:407-499.

    Not in our library

12. 12.

    Fabiani E, et al. Analysis of genome-wide methylation and gene expression induced by 5-aza-2’-deoxycytidine identifies BCL2L10 as a frequent methylation target in acute myeloid leukemia. Leuk. Lymphoma. 2010;51:2275-2284.

    Not in our library10.3109/10428194.2010.528093 (opens in a new tab)

13. 13.
    Breast tumor copy number aberration phenotypes and genomic instability · 2006

    No notice
14. 14.

    Glinsky GV. Integration of HapMap-based SNP pattern analysis and gene expression profiling reveals common SNP profiles for cancer therapy outcome predictor genes. Cell Cycle. 2006;5:2613-2625.

    Not in our library10.4161/cc.5.22.3498 (opens in a new tab)

15. 15.

    Hamid JS, et al. Data integration in genetics and genomics: methods and challenges. Hum. Genomics Proteomics. 2009;1:1-13.

    Not in our library10.4061/2009/869093 (opens in a new tab)

16. 16.

    Handolias D, et al. Clinical responses observed with imatinib or sorafenib in melanoma patients expressing mutations in KIT. Br. J. Cancer. 2010;102:1219-1223.

    Not in our library10.1038/sj.bjc.6605635 (opens in a new tab)

17. 17.

    Harrel FE. Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression. 2001.

    Not in our library

18. 18.
    Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies · 2008

    No notice
19. 19.

    Hudson TJ, et al. International network of cancer genome projects. Nature. 2010;464:993-998.

    Not in our library10.1038/nature08987 (opens in a new tab)

20. 20.

    Kang GH, et al. DNA methylation profiles of gastric carcinoma characterized by quantitative DNA methylation analysis. Lab Invest. 2008;88:161-170.

    Not in our library10.1038/labinvest.3700707 (opens in a new tab)

21. 21.

    Karpenko O, Dai Y. Relational database index choices for genome annotation data. Bioinformatics and Biomedicine Workshops (BIBMW). 2010.

    Not in our library

22. 22.

    Lanckriet GRG, et al. A statistical framework for genomic data fusion. Bioinformatics. 2004;20:2626-2635.

    Not in our library10.1093/bioinformatics/bth294 (opens in a new tab)

23. 23.

    Li J, et al. The Bayesian lasso for genome-wide association studies. Bioinformatics. 2011;27:516-523.

    Not in our library10.1093/bioinformatics/btq688 (opens in a new tab)

24. 24.

    Mohankumar KM, et al. Transcriptional activation of signal transducer and activator of transcription (STAT) 3 and STAT5B partially mediate homeobox A1-stimulated oncogenic transformation of the immortalized human mammary epithelial cell. Endocrinology. 2008;149:2219-2229.

    Not in our library10.1210/en.2007-1320 (opens in a new tab)

25. 25.

    Morris JS, et al. Bayesian analysis of mass spectrometry data using wavelet-based functional mixed models. Biometrics. 2008;64:479-489.

    Not in our library10.1111/j.1541-0420.2007.00895.x (opens in a new tab)

26. 26.

    Nanjundan M, et al. Amplification of MDS1/EVI1 and EVI1, located in the 3q26.2 amplicon, is associated with favorable patient prognosis in ovarian cancer. Cancer Res. 2007;67:3074-3084.

    Not in our library10.1158/0008-5472.can-06-2366 (opens in a new tab)

27. 27.

    Newton MA, et al. Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics. 2004;5:155-176.

    Not in our library10.1093/biostatistics/5.2.155 (opens in a new tab)

28. 28.

    Noushmehr H, et al. The cancer genome atlas research network, identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010;17:510-522.

    Not in our library10.1016/j.ccr.2010.03.017 (opens in a new tab)

29. 29.

    Park T, Casella G. The Bayesian lasso. J. Am. Stat. Assoc. 2008;103:681-686.

    Not in our library

30. 30.

    Park SY, et al. Promoter CpG island hypermethylation during breast cancer progression. Virchows Arch. 2011;458:73-84.

    Not in our library10.1007/s00428-010-1013-6 (opens in a new tab)

31. 31.

    Pegram M, Slamon D. Biological rationale for HER2/neu (c-erbB2) as a target for monoclonal antibody therapy. Semin. Oncol. 2000;5:13-19.

    Not in our library

32. 32.

    Peng J, et al. Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. Ann. Statist. 2010;4:53-77.

    Not in our library10.1214/09-aoas271supp (opens in a new tab)

33. 33.

    Qin LX. An integrative analysis of microRNA and mRNA expression-a case study. Cancer Inform. 2008;6:369-379.

    Not in our library10.4137/cin.s633 (opens in a new tab)

34. 34.

    Scott KL, et al. GOLPH3 modulates mTOR signalling and rapamycin sensitivity in cancer. Nature. 2009;459:1085-1090.

    Not in our library10.1038/nature08109 (opens in a new tab)

35. 35.

    Selamat SA, et al. DNA methylation changes in atypical adenomatous hyperplasia, adenocarcinoma in situ, and lung adenocarcinoma. PLoS One. 2011;6:e21443.

    Not in our library10.1371/journal.pone.0021443 (opens in a new tab)

36. 36.

    Storey JD, Tibshirani R. Statistical significance for genome-wide experiments. Proc. Natl Acad. Sci. USA. 2003;100:9440-9445.

    Not in our library10.1073/pnas.1530509100 (opens in a new tab)

37. 37.

    Tanner M, Wong W. The calculation of posterior distributions by data augmentation (with discussion). J. Am. Stat. Assoc. 1987;82:528-550.

    Not in our library

38. 38.

    Tseng CW, et al. Integrative network analysis reveals active microRNAs and their functions in gastric cancer. BMC Syst. Biol. 2011;5:99.

    Not in our library10.1186/1752-0509-5-99 (opens in a new tab)

39. 39.

    Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B. 1996;58:267-288.

    Not in our library

40. 40.

    Tomioka N, et al. Novel risk stratification of patients with neuroblastoma by genomic signature, which is independent of molecular signature. Oncogene. 2008;27:441-449.

    Not in our library10.1038/sj.onc.1210661 (opens in a new tab)

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