Abstract
A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.
Highlights
High-throughput omics platforms, such as mass spectrometry (MS) based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics, allow the comprehensive analysis of an organism’s reaction under different experimental conditions [1,2,3,4,5]
The two Metabolomics data sets derive from an Ultra Performance Liquid Chromatography (UPLC) analysis coupled to a Time-Of-Flight (TOF) MS detection
Estimation of covariances for dependent data sets, and the metaanalysis based on the previous results were applied and evaluated on the four Metabolomics/Transcriptomics data sets
Summary
High-throughput omics platforms, such as mass spectrometry (MS) based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics, allow the comprehensive analysis of an organism’s reaction under different experimental conditions [1,2,3,4,5]. A single data set usually contains the intensity/expression profiles (intensities for all measured samples) of thousands of features, such as different ion species in MS or spots in DNA microarray analysis. A popular method for this knowledge-based interpretation of single data sets is the Gene Set Enrichment Analysis [17] or Overrepresentation Analysis [18,19]. The enrichment analysis is based on sets of entities, e.g. pathways with associated metabolites, and results in a list of relevant sets which are enriched in high-ranking features (in comparison to all features in the data set). Modelling metabolic pathways as well-defined sets of biological entities, e.g. metabolites, enzymes, and corresponding genes, has shown to be a powerful approach to interpreting complex omics data sets. The concept of pathways associated with different types of biological entities facilitates the joint analysis of different data sets [24]
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