Title |
Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer
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Published in |
Genome Medicine, June 2016
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DOI | 10.1186/s13073-016-0319-7 |
Pubmed ID | |
Authors |
Safiye Celik, Benjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins, Su-In Lee |
Abstract |
Patterns in expression data conserved across multiple independent disease studies are likely to represent important molecular events underlying the disease. We present the INSPIRE method to infer modules of co-expressed genes and the dependencies among the modules from multiple expression datasets that may contain different sets of genes. We show that INSPIRE infers more accurate models than existing methods to extract low-dimensional representation of expression data. We demonstrate that applying INSPIRE to nine ovarian cancer datasets leads to a new marker and potential driver of tumor-associated stroma, HOPX, followed by experimental validation. The implementation of INSPIRE is available at http://inspire.cs.washington.edu . |
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Members of the public | 3 | 33% |
Mendeley readers
Geographical breakdown
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Netherlands | 1 | 2% |
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Readers by professional status | Count | As % |
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Student > Ph. D. Student | 14 | 24% |
Other | 7 | 12% |
Student > Bachelor | 4 | 7% |
Student > Doctoral Student | 3 | 5% |
Other | 9 | 15% |
Unknown | 7 | 12% |
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Medicine and Dentistry | 11 | 19% |
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Engineering | 5 | 8% |
Other | 4 | 7% |
Unknown | 7 | 12% |