期刊
BRIEFINGS IN BIOINFORMATICS
卷 14, 期 1, 页码 27-35出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbs005
关键词
DNA copy number; gene expression; microarrays; data integration; algorithms; cancer
资金
- EuGESMA COST Action (European Genomics and Epigenomics Study on MDS and AML) [BM0801]
- Helsinki Institute for Information Technology HIIT
- Finnish Center of Excellence on Adaptive Informatics Research (AIRC)
- European Leukemia Network of Excellence [LSHC-CT-2004]
- Deutsche Kinderkrebsstiftung [DKS 2010.21]
- Carreras Foundation [DJCLS 09/04]
- Deutsche Forschungsgemeinschaft (Research Training Group Statistical Modeling)
- AIRC Special Program Molecular Clinical Oncology '5 per mille'
A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.
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