Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Celik, Safiye; Logsdon, Benjamin; Battle, Stephanie; Drescher, Charles; Rendi, Mara; Hawkins, R.; Lee, Su-In
• Is Part Of: Genome medicine. Volume 8:Issue 1(2016)
• Page Start: 1
• Page End: 31
• Publication Date: 2016-12
• Subjects: Gene expression -- Variable discrepancy -- Low-dimensional representation -- Module -- Conditional dependence -- Latent variable -- HOPX -- Tumor-associated stroma; Genomics -- Periodicals; Medical genetics -- Periodicals; 616.042
• DOI: 10.1186/s13073-016-0319-7
• Languages: English
• ISSNs: 1756-994X

Abstract

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 athttp://inspire.cs.washington.edu .