Jing Ma1, Ali Shojaie2, George Michailidis3. 1. Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, PA 19104, USA. 2. Department of Biostatistics, University of Washington, Seattle, WA 98915, USA. 3. Department of Statistics, University of Florida, Gainesville, FL 32611, USA.
Abstract
MOTIVATION: Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight into the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a system-level view of changes in cellular activity in response to treatments and/or in disease states. Methods that use existing pathway network information have been shown to outperform simpler methods that only take into account pathway membership. However, despite significant progress in understanding the association amongst members of biological pathways, and expansion of data bases containing information about interactions of biomolecules, the existing network information may be incomplete or inaccurate and is not cell-type or disease condition-specific. RESULTS: We propose a constrained network estimation framework that combines network estimation based on cell- and condition-specific high-dimensional Omics data with interaction information from existing data bases. The resulting pathway topology information is subsequently used to provide a framework for simultaneous testing of differences in expression levels of pathway members, as well as their interactions. We study the asymptotic properties of the proposed network estimator and the test for pathway enrichment, and investigate its small sample performance in simulated and real data settings. AVAILABILITY AND IMPLEMENTATION: The proposed method has been implemented in the R-package netgsa available on CRAN. CONTACT: jinma@upenn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
MOTIVATION: Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight into the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a system-level view of changes in cellular activity in response to treatments and/or in disease states. Methods that use existing pathway network information have been shown to outperform simpler methods that only take into account pathway membership. However, despite significant progress in understanding the association amongst members of biological pathways, and expansion of data bases containing information about interactions of biomolecules, the existing network information may be incomplete or inaccurate and is not cell-type or disease condition-specific. RESULTS: We propose a constrained network estimation framework that combines network estimation based on cell- and condition-specific high-dimensional Omics data with interaction information from existing data bases. The resulting pathway topology information is subsequently used to provide a framework for simultaneous testing of differences in expression levels of pathway members, as well as their interactions. We study the asymptotic properties of the proposed network estimator and the test for pathway enrichment, and investigate its small sample performance in simulated and real data settings. AVAILABILITY AND IMPLEMENTATION: The proposed method has been implemented in the R-package netgsa available on CRAN. CONTACT: jinma@upenn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Robert J Prill; Daniel Marbach; Julio Saez-Rodriguez; Peter K Sorger; Leonidas G Alexopoulos; Xiaowei Xue; Neil D Clarke; Gregoire Altan-Bonnet; Gustavo Stolovitzky Journal: PLoS One Date: 2010-02-23 Impact factor: 3.240
Authors: Michael Wainberg; Daniele Merico; Matthew C Keller; Eric B Fauman; Shreejoy J Tripathy Journal: Mol Psychiatry Date: 2022-04-11 Impact factor: 15.992
Authors: Venkatrao Vantaku; Vasanta Putluri; David A Bader; Suman Maity; Jing Ma; James M Arnold; Kimal Rajapakshe; Sri Ramya Donepudi; Friedrich-Carl von Rundstedt; Vaishnavi Devarakonda; Julien Dubrulle; Balasubramanyam Karanam; Sean E McGuire; Fabio Stossi; Abhinav K Jain; Cristian Coarfa; Qi Cao; Andrew G Sikora; Hugo Villanueva; Shyam M Kavuri; Yair Lotan; Arun Sreekumar; Nagireddy Putluri Journal: Oncogene Date: 2019-08-05 Impact factor: 9.867
Authors: Sandi L Navarro; Aliasghar Tarkhan; Ali Shojaie; Timothy W Randolph; Haiwei Gu; Danijel Djukovic; Katie J Osterbauer; Meredith A Hullar; Mario Kratz; Marian L Neuhouser; Paul D Lampe; Daniel Raftery; Johanna W Lampe Journal: Am J Clin Nutr Date: 2019-10-01 Impact factor: 7.045