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Activity Number: 306
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #313143
Title: Assign: Context-Specific and Integrative Genomic Profiling of Heterogenous Biological Pathways
Author(s): Ying Shen*+ and W. Evan Johnson
Companies: Boston University and Boston University School of Medicine
Keywords: pathway signature ; gene expression data ; Bayesian factor analysis ; context-specific annotation
Abstract:

Even with the recent accumulation of high-throughput genomic data, profiling disease-causing pathway in patients remains a challenge due to the lack of cell- or tissue-specific pathway annotation and efficient statistical and computational tools. Addressing these challenges enable us to elucidate disease mechanisms and provide guidance for personalized therapeutics. Here, we present a novel algorithm for personalized pathway analysis - ASSIGN that efficiently captures pathway deregulation/activation status in individual patient samples. ASSIGN utilizes a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease contexts. We demonstrate validity and robustness of our model in predicting pathway activity levels in cancer cell lines and linking activity to drug responsiveness. In cancer patients, we are able to predict pathway deregulation and efficiently distinguish between normal and tumor tissue subtypes. ASSIGN is initially designed for pathway-based analysis on gene expression data; nevertheless it can be extended to genome-wide SNP or methylation data.


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