PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples

Odom, Gabriel J. and Colaprico, Antonio and Silva, Tiago C. and Chen, X. Steven and Wang, Lily (2021) PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples. Frontiers in Genetics, 12. ISSN 1664-8021

[thumbnail of pubmed-zip/versions/2/package-entries/fgene-12-783713-r1/fgene-12-783713.pdf] Text
pubmed-zip/versions/2/package-entries/fgene-12-783713-r1/fgene-12-783713.pdf - Published Version

Download (1MB)

Abstract

Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductions, which extract association signals in high dimensional datasets by estimating a few variables that explain most of the variations in the samples, are typically applied to whole-genome data, which can be computationally demanding. Here we present pathwayMultiomics, a pathway-based approach for integrative analysis of multi-omics data with categorical, continuous, or survival outcome variables. The input of pathwayMultiomics is pathway p-values for individual omics data types, which are then integrated using a novel statistic, the MiniMax statistic, to prioritize pathways dysregulated in multiple types of omics datasets. Importantly, pathwayMultiomics is computationally efficient and does not require matched samples in multi-omics data. We performed a comprehensive simulation study to show that pathwayMultiomics significantly outperformed currently available multi-omics tools with improved power and well-controlled false-positive rates. In addition, we also analyzed real multi-omics datasets to show that pathwayMultiomics was able to recover known biology by nominating biologically meaningful pathways in complex diseases such as Alzheimer’s disease.

Item Type: Article
Subjects: STM Library > Medical Science
Depositing User: Managing Editor
Date Deposited: 03 Feb 2023 07:21
Last Modified: 15 Jan 2024 04:12
URI: http://open.journal4submit.com/id/eprint/1459

Actions (login required)

View Item
View Item