High-dimensional sparse factor modeling: Applications in gene expression genomics
作者： Carlos M. Carvalho , Jeffrey Chang , Joseph E. Lucas , Joseph R. Nevins , Quanli Wang
DOI: 10.1198/016214508000000869
关键词:
摘要: We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor regression models for microarray gene expression data. discuss breast cancer applications key aspects of the modeling computational methodology. Our case aim to investigate characterize heterogeneity structure related specific oncogenic pathways, as well links between aggregate patterns profiles clinical biomarkers. Based on metaphor statistically derived “factors” representing “subpathway” structure, we explore decomposition fitted into subcomponents how these components overlay multiple known activity. methodology is based sparsity multivariate regression, ANOVA, models, a class combines all components. Hierarchical priors address questions dimension reduction co...
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