Abstract
BackgroundPathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.ResultsWe introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets –three from Homo sapiens, two from Danio rerio and one from Mus musculus– and has reproduced findings from the original studies and from independent literature.ConclusionsThe R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.
Highlights
ResultsWe introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites
Pathway enrichment techniques are useful for understanding experimental metabolomics data
Block II: enrichment analysis Once the database is loaded, i.e. the FELLA.DATA object is in memory, defineCompounds() maps the list of input metabolites, in the form of Kyoto Encyclopedia of Genes and Genomes (KEGG) identifiers, to the internal representation, providing a FELLA.USER object
Summary
The algorithmic part of FELLA has already been discussed and validated in [12]. The usage of FELLA is hereby demonstrated on three public human studies on epithelial cells [17], ovarian cancer cells [18] and febrile illnesses [19]. The sub-network is obtained by leaving the default parameters and setting a limit of nlimit = 50 nodes In this case, the depicted subnetwork contains the modules “C21-Steroid hormone biosynthesis, progesterone =>corticosterone/aldosterone” and “C21-Steroid hormone biosynthesis, progesterone =>cortisol/cortisone”, related to the “corticosteroids” as a main pathway reported in the original text. The study justified its findings through literature and complemented them with insights provided by FELLA Both metabolite lists are used to build suggested sub-networks with the default parameters and fixing nlimit = 250. From which “Linoleic acid metabolism”, “Biosynthesis of unsaturated fatty acids”, “alpha-Linolenic acid metabolism”, “Glycerophospholipid metabolism” and “Glycine, serine and threonine metabolism” were used to build a comprehensive picture of the metabolic changes in the original manuscript (Fig. 3 from [20]) Such figure brings a structured overview that narrows down the core processes, backed up by prior publications. We show how other (1) related metabolites, found by leveraging the expression data, and (2) differentially expressed genes, taken from an external study [33], tend to have top p-scores in the prioritisation provided by FELLA
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