High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics.

ABSTRACT: We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on spa

SUBMITTER: Carvalho CM 

PROVIDER: S-EPMC3017385 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature