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Issue No. 06 - Nov.-Dec. (2014 vol. 11)
ISSN: 1545-5963
pp: 984-994
Karl B. Gregory , Department of Statistics, Texas A&M University, College Station, TX
Amin A. Momin , Department of Clinical Cancer Prevention, the University of Texas MD Anderson Cancer Center, Houston, TX
Kevin R. Coombes , Department of Biomedical Informatics, the Ohio State University Wexner Medical Center, Columbus, OH
Veerabhadran Baladandayuthapani , Department of Biostatistics, the University of Texas MD Anderson Cancer Center, Houston, TX
Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to a glioblastoma multiforme data set from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between genomic platforms to aid accurate prediction and variable selection. Our methods perform best when principal components are used to define the latent features.
Bioinformatics, Data models, Genomics, Predictive models, Analytical models, Cancer, Matrix decomposition,Bayesian model averaging, Latent feature, genomic data, high-dimensional, interactions, integrative models
Karl B. Gregory, Amin A. Momin, Kevin R. Coombes, Veerabhadran Baladandayuthapani, "Latent Feature Decompositions for Integrative Analysis of Multi-Platform Genomic Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. , pp. 984-994, Nov.-Dec. 2014, doi:10.1109/TCBB.2014.2325035
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