Abstract
BackgroundEnrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses.ResultsWe show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question.ConclusionsThe choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.
Highlights
Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets
Researchers have explored a number of mathematical methods for calculating pathway activity levels and pathway abundances, but all of these methods depend on a collection of pathways within pathway databases (DBs) such as BioCyc [2], Kyoto Encyclopedia of Genes and Genomes (KEGG) [3], and Reactome [4]
The second question we investigate is: If a given pathway has a high enrichment score, what does this result tell us biologically? That is, what have we learned about the biological system under study? For example, does the pathway clearly correspond to one biological process, or does the pathway integrate so many biological processes that the biological significance of identifying that pathway as enriched is of little meaning? We show that the answers to Questions 1 and 2 depend on the pathway database being used
Summary
Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Pathway analysis has become a popular way to analyze gene expression data This family of analysis methods seeks to find which biological processes have changed their activity levels most significantly across two different biological states [1]. Researchers have explored a number of mathematical methods for calculating pathway activity levels and pathway abundances, but all of these methods depend on a collection of pathways within pathway databases (DBs) such as BioCyc [2], KEGG [3], and Reactome [4] (some methods use the biological processes defined in Gene Ontology [5]). The first question we ask in this article is: to what degree does the choice of pathway database affect the results returned by a pathway-analysis method?
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