REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES

• 成果类型: Article
• 署名作者: Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.
• 署名单位: University of California System; University of California Santa Barbara; University of Cambridge; Cancer Research UK; CRUK Cambridge Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS915
• 发表日期: 2018
• 页码: 1361-1384
• 关键词: nested effects models; gene-expression; regulatory networks; protein-interaction; signaling pathways; identification; refinement; microarray; landscape; inference

摘要

Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.