Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway | Zendy

Pengyi Yang | Zendy; Ellis Patrick | Zendy; ShiXiong Tan | Zendy; Daniel J. Fazakerley | Zendy; James G. Burchfield | Zendy; Christopher Gribben | Zendy; Matthew J. Prior | Zendy; David E. James | Zendy; Jean Yang | Zendy

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Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway

Author(s) -

Pengyi Yang,

Ellis Patrick,

ShiXiong Tan,

Daniel J. Fazakerley,

James G. Burchfield,

Christopher Gribben,

Matthew J. Prior,

David E. James,

Jean Yang

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt616

Subject(s) - computational biology , proteomics , context (archaeology) , biological pathway , biology , insulin , computer science , chemistry , bioinformatics , microbiology and biotechnology , gene , genetics , gene expression , paleontology , endocrinology

With the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes/proteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.e. up, down, either or mixed regulation). Here, we develop direction pathway analysis (DPA), which can be applied to test hypothesis in a high-dimensional space for identifying pathways that display distinct responses across multiple perturbations.

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