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PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi‐Omics Data
Author(s) -
Odom Gabriel J.,
Ban Yuguang,
Colaprico Antonio,
Liu Lizhong,
Silva Tiago Chedraoui,
Sun Xiaodian,
Pico Alexander R.,
Zhang Bing,
Wang Lily,
Chen Xi
Publication year - 2020
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201900409
Subject(s) - bioconductor , r package , covariate , computer science , principal component analysis , pathway analysis , data mining , computational biology , gene selection , data science , bioinformatics , biology , machine learning , microarray analysis techniques , artificial intelligence , gene , biochemistry , gene expression , computational science
The authors present pathwayPCA , an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic‐net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease‐associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample‐specific pathway activities, identifying sex‐specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open‐source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.