Title |
Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline
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Published in |
BMC Bioinformatics, December 2013
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DOI | 10.1186/1471-2105-14-368 |
Pubmed ID | |
Authors |
Lun-Ching Chang, Hui-Min Lin, Etienne Sibille, George C Tseng |
Abstract |
As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Norway | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 3 | 2% |
Germany | 2 | 1% |
Netherlands | 1 | <1% |
Brazil | 1 | <1% |
New Caledonia | 1 | <1% |
Sweden | 1 | <1% |
United States | 1 | <1% |
Unknown | 164 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 48 | 28% |
Researcher | 36 | 21% |
Student > Master | 15 | 9% |
Student > Bachelor | 14 | 8% |
Other | 11 | 6% |
Other | 27 | 16% |
Unknown | 23 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 47 | 27% |
Biochemistry, Genetics and Molecular Biology | 31 | 18% |
Medicine and Dentistry | 24 | 14% |
Computer Science | 19 | 11% |
Mathematics | 8 | 5% |
Other | 14 | 8% |
Unknown | 31 | 18% |