Title |
Genome-wide identification of significant aberrations in cancer genome
|
---|---|
Published in |
BMC Genomics, July 2012
|
DOI | 10.1186/1471-2164-13-342 |
Pubmed ID | |
Authors |
Xiguo Yuan, Guoqiang Yu, Xuchu Hou, Ie-Ming Shih, Robert Clarke, Junying Zhang, Eric P Hoffman, Roger R Wang, Zhen Zhang, Yue Wang |
Abstract |
Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 2 | 50% |
United States | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 7% |
India | 1 | 2% |
Lithuania | 1 | 2% |
Denmark | 1 | 2% |
United Kingdom | 1 | 2% |
Unknown | 39 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 15 | 33% |
Student > Ph. D. Student | 10 | 22% |
Student > Bachelor | 6 | 13% |
Student > Postgraduate | 4 | 9% |
Professor > Associate Professor | 3 | 7% |
Other | 5 | 11% |
Unknown | 3 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 20 | 43% |
Medicine and Dentistry | 11 | 24% |
Biochemistry, Genetics and Molecular Biology | 5 | 11% |
Computer Science | 4 | 9% |
Unknown | 6 | 13% |