Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer.
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 .
Department of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
Safiye
S
Celik
Sage Bionetworks, Seattle, WA, USA.
Benjamin A
BA
Logsdon
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Stephanie
S
Battle
Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Charles W
CW
Drescher
Department of Anatomic Pathology, University of Washington, Seattle, WA, USA.
Mara
M
Rendi
Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
R David
RD
Hawkins
Department of Computer Science & Engineering, University of Washington, Seattle, WA, USA. suinlee@uw.edu.|Department of Genome Sciences, University of Washington, Seattle, WA, USA. suinlee@uw.edu.
Su-In
SI
Lee
D014408:Biomarkers, Tumor / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D018398:Homeodomain Proteins / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D010051:Ovarian Neoplasms / Q000235:genetics*; D025521:Tumor Suppressor Proteins / Q000235:genetics* / Q000378:metabolism; D000069558:Unsupervised Machine Learning
D014408:Biomarkers, Tumor; C476884:HOPX protein, human; D018398:Homeodomain Proteins; D025521:Tumor Suppressor Proteins
Conditional dependence; Gene expression; HOPX; Latent variable; Low-dimensional representation; Module; Tumor-associated stroma; Variable discrepancy