Journal article
Similarity of markers identified from cancer gene expression studies: observations from GEO
Briefings in bioinformatics, Vol.15(5), pp.671-684
09/2014
DOI: 10.1093/bib/bbt044
PMCID: PMC4271059
PMID: 23788798
Abstract
Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data.
Details
- Title: Subtitle
- Similarity of markers identified from cancer gene expression studies: observations from GEO
- Creators
- Xingjie Shi - Shanghai University of Finance and EconomicsShihao Shen - University of California, Los AngelesJin Liu - Yale UniversityJian Huang - University of IowaYong Zhou - Shanghai University of Finance and EconomicsShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- Briefings in bioinformatics, Vol.15(5), pp.671-684
- DOI
- 10.1093/bib/bbt044
- PMID
- 23788798
- PMCID
- PMC4271059
- ISSN
- 1467-5463
- eISSN
- 1477-4054
- Grant note
- CA142774 / NCI NIH HHS P30 CA016359 / NCI NIH HHS DMS1208225 / PHS HHS CA152301 / NCI NIH HHS CA165923 / NCI NIH HHS
- Language
- English
- Date published
- 09/2014
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257623302771
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