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

Autor:	Odom GJ; Department of Biostatistics, Stempel College of Public Health, Florida International University, Miami, FL, United States.; Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States., Colaprico A; Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States., Silva TC; Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States., Chen XS; Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States., Wang L; Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States.; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States.; Dr. John T Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, United States.; John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2021 Dec 22; Vol. 12, pp. 783713. Date of Electronic Publication: 2021 Dec 22 (Print Publication: 2021).
DOI:	10.3389/fgene.2021.783713
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Odom, Colaprico, Silva, Chen and Wang.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu