Work

Detecting differential patterns of interaction in molecular pathways

Year: 2014

Type: article

Abstract: We consider statistical inference for potentially heterogeneous patterns of association characterizing the expression of bio-molecular pathways across different biologic conditions. We discuss a modeling approach based on Gaussian-directed acyclic graphs and provide computational and methodological details needed for posterior inference. Our application finds motivation in reverse phase protein array data from a study on acute myeloid leukemia, where interest centers on contrasting refractory versus relapsed patients. We illustrate the proposed method through both synthetic and case study data.

Source: Biostatistics

DOI: 10.1093/biostatistics/kxu054

Authors: M. Yajima, D. Telesca, Y. Ji, P. Muller

Institutions: Fred Hutch Cancer Center, University of California, Los Angeles, Endeavor Health, The University of Texas at Austin

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Institutes of Health, National Cancer Institute

Awards: Population Pharmacokinetics/Dynamic: Statistical Issues, Consistent variable selection in p>>n settings, G5520316217, Bayesian Inference for Tumor Heterogeneity with Next-Generation Sequencing Data, G6569300465