Abstract
Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.
Highlights
Pathway analysis (PA) plays a vital role in the interpretation of high-dimensional molecular data
Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community
We offer the research community a set of best-practice recommendations applicable to ORA and to other pathway analysis methods to help ensure the reliability and reproducibility of results
Summary
Pathway analysis (PA) plays a vital role in the interpretation of high-dimensional molecular data. It is used to find associations between pathways, which represent collections of molecular entities sharing a biological function, and a phenotype of interest [1]. Based on existing knowledge of biological pathways, molecular entities such as genes, proteins, and metabolites can be mapped onto curated pathway sets, which aim to represent how these entities collectively function and interact in a biological context [2]. Metabolomics datasets tend to cover a much lower proportion of the total metabolome than transcriptomic datasets do of the genome. Metabolomics datasets tend to contain far fewer metabolites than transcripts found in transcriptomic datasets. Mapping compounds to pathways is not as straightforward as the equivalent mapping with genes and proteins, and there is often a significant level of uncertainty surrounding metabolite identification, both with respect to structures and database identifiers in any metabolomics dataset
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