High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics.
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics.
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DOI：
10.1198/016214508000000869
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发表时间：
2008-12-01
影响因子：
3.7
通讯作者：
West M
中科院分区：
文献类型：
--
作者：
Carvalho CM;Chang J;Lucas JE;Nevins JR;Wang Q;West M
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 sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.
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影响因子：
3.7
作者：
DiCiccio, TJ;Kass, RE;Wasserman, L
通讯作者：
Wasserman, L
DOI：
10.1111/j.1467-9876.2005.05593.x
发表时间：
2005-01-01
影响因子：
1.6
作者：
Do, KA;Müller, P;Tang, F
通讯作者：
Tang, F
影响因子：
168.9
作者：
Huang, E;Cheng, SH;Huang, AT
通讯作者：
Huang, AT
DOI：
10.1198/016214507000000121
发表时间：
2007-06-01
影响因子：
3.7
作者：
Hans, Chris;Dobra, Adrian;West, Mike
通讯作者：
West, Mike
影响因子：
3.7
作者：
GEORGE, EI;MCCULLOCH, RE
通讯作者：
MCCULLOCH, RE