PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi‐Omics Data

• Published in: Proteomics (Weinheim) Vol. 20; no. 21-22; pp. e1900409 - n/a
• Main Authors: Odom, Gabriel J., Ban, Yuguang, Colaprico, Antonio, Liu, Lizhong, Silva, Tiago Chedraoui, Sun, Xiaodian, Pico, Alexander R., Zhang, Bing, Wang, Lily, Chen, Xi
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01-11-2020
• Subjects: Case studies; Computational Biology; Consortia; Genomics; Humans; integrative genomics analysis; Kidney cancer; Medical prognosis; pathway analysis; principal component analysis; Principal components analysis; Proteomics; Software; pathway analysis; integrative genomics analysis; principal component analysis
• ISSN: 1615-9853; 1615-9861
• DOI: 10.1002/pmic.201900409

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.