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.
Highlights
Recent advances in technology have made multi-omics datasets increasingly available to researchers
We showed that pathwayMultiomics significantly outperforms currently available multi-omics methods with improved power and well-controlled false-positive rates
Because sparse multiple CCA (mCCA) lacked power even in the last simulation scenario with the strongest signal (80% genes in a true positive pathway are treated with an effect size of 0.5), we only included area under the receiver operating characteristic (ROC) curve (AUC) for sparse mCCA in the last simulation scenario
Summary
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 (Meng et al, 2016; Huang et al, 2017) and compared (Le Cao et al, 2009; Pucher et al, 2019). These methods can be roughly classified into three different categories, characterized by the way they leverage information from the multi-omics datasets. The third group of methods (Gao et al, 2004; Kutalik et al, 2008; Zhang et al, 2012; Meng et al, 2014) analyzes matched samples in multi-omics datasets, where each dataset may have the same or distinct genes
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