PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples
Year: 2021
Type: article
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.
Source: Frontiers in Genetics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,950
Awards: Integrative prediction models for metastasis risk in colon cancer, Epidemiologic Study of Neural Reserve and Neurobiology of Aging, Building blood based DNA methylation signatures for AD that are reflective of CNS changes, Genetic Epidemiology of Cognitive Decline in an Aging Population Sample, Exploring the Role of the Brain Transcriptome in Cognitive Decline, Pathway discovery, validation and compound identification for Alzheimer's disease, G4298697694, G4356727394, Risk Factors, Pathology, and Clinical Expressions of AD, G5018925220, Rush Alzheimer's Disease Core Center, Alzheimer's Disease Genetics Consortium, Integrative statistical models for TNBC biomarker discovery, G541333419, G5713958695, G578809140, G5952929075, G7479973302, G7906889984, Integrative Genomic Approaches for Understanding Sex Differences in Alzheimer's Disease, New statistical strategies for comprehensive analysis of epigenomewide methylation data, The Sylvester Cancer Center Support Grant
OpenAlex ID: W4200293873