Conceptual aspects of large meta-analyses with publicly available microarray data: A case study in oncology

Bioinformatics and Biology Insights

Volumn 5, Issue , 2011, Pages 13-39

  Schmidberger, Markus ^a   Lennert, Sabine ^a   Mansmann, Ulrich ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Gene graphs; Meta analysis; Oncology; Public microarray data; R

Indexed keywords

ACUTE GRANULOCYTIC LEUKEMIA; ADULT; ARTICLE; BIOINFORMATICS; BIOSTATISTICS; CANCER RESEARCH; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DATA BASE; FEASIBILITY STUDY; FEMALE; HUMAN; MALE; META ANALYSIS (TOPIC); MICROARRAY ANALYSIS; MISMATCH REPAIR; NEOPLASM; PHENOTYPE; QUALITY CONTROL; SOLID TUMOR;

EID: 79952963743     PISSN: None     EISSN: 11779322     Source Type: Journal    
DOI: 10.4137/BBI.S5537     Document Type: Article

References (39)

1. Thomas J Hudson, Warwick Anderson, Axel Artez, et al. International Cancer Genome Consortium. International network of cancer genome projects. Nature. 2010 Apr;464(7291):993-8.
2. Baisse B, Bouzourene H, Saraga EP, Bosman FT, Ben-Hattar J. Intratumor genetic heterogeneity in advanced human colorectal adenocarcinoma. Int J Cancer. 2001 Aug;93(3):346-52.
3. Thomas Brabletz, Andreas Jung, Simone Spaderna, Falk Hlubek, Thomas Kirchner. Opinion: migrating cancer stem cells-an integrated concept of malignant tumour progression. Nat Rev Cancer. 2005 Sep;5(9):744-9.
4. Daniel R. Rhodes, Jianjun Yu, Shanker K, et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A. 2004 Jun;101(25):9309-14.
5. Alvis Brazma. Minimum information about a microarray experiment (MIAME)-towards standards for microarray data. Nature Genetics. 2001; 29:365-71.
6. Catherine A Ball, Alvis Brazma, Helen Causton, et al. Submission of microarray data to public repositories. PLoS Biology. 2004 Aug;2(9):e317.
7. Gardiner-Garden M, Littlejohn TG. A comparison of microarray databases. Brief Bioinform. 2001 May;2(2):143-58.
8. Brazma Alvis, Parkinson Helen, Sarkans Ugis, et al. ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Research. 2003 Jan;31(1):68-71.
9. Barrett Tanya, Troup Dennis B, Wilhite Stephen E, et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Research. 2009 Jan;37(Database issue):D885-90.
10. Kazuho Ikeo, Jun Ishi-i, Takurou Tamura, Takashi Gojobori, Yoshio Tateno. CIBEX: center for information biology gene expression database. C R Biol. 2003;326(10-11):1079-82.
11. Erin M Conlon. A bayesian mixture model for meta-analysis of microarray studies. Funct Integr Genomics. 2008 Feb;8(1):43-53.
12. Landgren O, Pfeiffer RM, Stewart L, et al. Risk of second malignant neoplasms among lymphoma patients with a family history of cancer. International Journal of Cancer. 2007;1099-1102(5):8-14.
13. Lynch H, Family with acute myelocytic leukemia, breast, ovarian, and gastrointestinal cancer. Cancer Genetics and Cytogenetics. 2009;137(1): 8-14.
14. Brandt A, Bermejo JL, Sundquist J, Hemminki K. Risk of second malignant neoplasms among lymphoma patients with a family history of cancer. European Journal of Cancer. 2009. [Epub ahead of print].
15. Arinobu Y, Mizuno SI, Chong Y, et al. Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell. 2007;1(4): 416-27.
16. Ulrich Mansmann, Markus Schmidberger, Ralf Strobl, Vindi Jurinovic. Statistical modelling and regression structures-festschrift in honour of Ludwig Fahrmeir, chapter Indirect Comparison of Interaction Graphs. Physica, 2009:249-65.
17. Adaikalavan Ramasamy, Adrian Mondry, Chris C Holmes, Douglas G Altman. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Medicine. 2008 Sep;5(9):e184.
18. R development core team. R: A language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing; 2009. ISBN 3-900051-07-0.
19. Robert C Gentleman, Vincent J Carey, Douglas M Bates, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biology. 2004:5.
20. Markus Schmidberger, Martin Morgan, Dirk Eddelbuettel, Hao Yu, Luke Tierney, Ulrich Mansmann. State of the art in parallel computing with R. Journal of Statistical Software. 2009;31(1).
21. Markus Schmidberger. Parallel Computing for Biological Data. PhD thesis, 2009 Nov.
22. Markus Schmidberger, Ulrich Mansmann. Parallelized pre-processing algorithms for high-density oligonucleotide arrays. In Proceedings IEEE International Symposium on Parallel and Distributed Processing IPDPS 2008. 2008 Apr 14-18:1-7.
23. Markus Schmidberger, Ulrich Mansmann. affyPara-a bio-conductor package for parallelized preprocessing algorithms of affymetrix microarray data. Bioinformatics and Biology Insights. 2009;3:83-7.
24. Audrey Kauffmann, Tim F Rayner, Helen Parkinson, et al. Importing ArrayExpress datasets into R/Bioconductor. Bioinformatics. 2009;25(16): 2092-4.
25. Robert C Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit. Bioinformatics and Computational Biology Solutions using R and Bioconductor, 1st ed. Springer; 2005 Aug.
26. Rafael A Irizarry, Daniel Warren, Forrest Spencer, et al. Multiple-laboratory comparison of microarray platforms. Nature Methods. 2005 May;2(5): 345-50.
27. Evan Johnson W, Cheng Li, Ariel Rabinovic. Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics. 2007 Jan;8(1):118-27.
28. Markus Kalisch, Peter Buehlmann. Estimating high dimensional acyclic graphs with the PC-Algorithm. Journal of Machine Learning Research. 2007;8:613-36.
29. Peter Spirtes, Clark Glymour, Richard Scheines. Causation, Prediction, and Search, 2nd ed. Cambridge, MA, USA: The MIT Press; 2001 Jan.
30. Fanny Villers, Brigitte Schaeffer, Caroline Bertin, Sylvie Huet. Assessing the validity domains of graphical Gaussian models in order to infer relationships among components of complex biological systems. Statistical Applications in Genetics and Molecular Biology. 2008;7(1):Article 14.
31. Juliane Schaefer, Korbinian Strimmer. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology. 2005;4: Article 32.
32. Nicolai Meinshausen, Peter Buehlmann. High dimensional graphs and variable selection with the Lasso. The Annals of Statistics. 2006;34:1436-62.
33. Martin J Wainwright, Pradeep Ravikumar, John D Lafferty. High dimensional graphical model selection using L1-regularized logistic regression. Proceedings of Advances in Neural Information Processing Systems. 2006; 9:1465-72.
34. Jerome Friedman, Trevor Hastie, Robert Tibshirani. Sparse inverse covariance estimation with the graphical Lasso. Biostatistics. 2007;9(3):432-41.
35. Banerjee Onureena, El Ghaoui Laurent, d'Aspremont Alexandre. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. Journal of Machine Learning Research. 2008: 485-516.
36. Aedn C Culhane, Thomas Schwarzl, Razvan Sultana, et al. Genesigdb-a curated database of gene expression signatures. Nucleic Acids Res. 2010 Jan; 38(Database issue):D716-25.
37. Howard Y Chang, Dimitry SA Nuyten, Julie B Sneddon, et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A. 2005 Mar;102(10):3738-43.
38. Alvis Brazma. Minimum information about a microarray experiment (MIAME)-successes, failures, challenges. Scientific World Journal. 9:420-3.
39. Arnold Ludwig. Random Dynamical Systems, 2nd ed. Berlin, Germany: Springer-Verlag; 1998 Jan.