Leqi Tian1,2, Zhenjiang Li3, Guoxuan Ma2,4, Xiaoyue Zhang3, Ziyin Tang3, Siheng Wang2, Jian Kang4, Donghai Liang3, Tianwei Yu1,2,5. 1. Shenzhen Research Institute of Big Data. 2. School of Data Science, The Chinese University of Hong Kong-Shenzhen. 3. Gangarosa Department of Environmental Health, Emory University. 4. Department of Biostatistics, University of Michigan. 5. Warshel Institute, Shenzhen, Guangdong, China.
Abstract
MOTIVATION: Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (1) matching uncertainty between data features and metabolites; (2) lacking of method to analyze positive mode and negative mode LC/MS data simultaneously on the same set of subjects; (3) the incompleteness of pathways in individual software packages. RESULTS: We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration - (1) a weighted GSEA-type test, and (2) a permutation-based weighted hypergeometric test. The package is capable of combining positive and negative ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: KEGG, Mummichog, and SMPDB. AVAILABILITY: The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (1) matching uncertainty between data features and metabolites; (2) lacking of method to analyze positive mode and negative mode LC/MS data simultaneously on the same set of subjects; (3) the incompleteness of pathways in individual software packages. RESULTS: We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration - (1) a weighted GSEA-type test, and (2) a permutation-based weighted hypergeometric test. The package is capable of combining positive and negative ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: KEGG, Mummichog, and SMPDB. AVAILABILITY: The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. SUPPLEMENTARY 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: Shuzhao Li; Youngja Park; Sai Duraisingham; Frederick H Strobel; Nooruddin Khan; Quinlyn A Soltow; Dean P Jones; Bali Pulendran Journal: PLoS Comput Biol Date: 2013-07-04 Impact factor: 4.475