A comparative study of topology-based pathway enrichment analysis methods

Abstract

BackgroundPathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples.ResultsThe findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment.ConclusionThe analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

Combinatorial Strategy for Studying Biochemical Pathways in Double Emulsion Templated Cell‐Sized Compartments

“…The first enzyme, β-gal, hydrolyses fluoresceindi-β-galactopyranoside (FGP) into fluorescein and galactose; in the absence of glucose, GOx can oxidize galactose and produce hydrogen peroxide (H 2 O 2 ), [72] which is used by HRP to oxidize Amplex Red (AR) into resorufin. This cascade performed by well-known enzymes allows accurate identification of which phenomena are due to diffusion; [73][74][75] additionally, the choice of FGP instead of the canonical lactose allows two reactions to be followed fluorometrically, and to infer the behavior of the middle one that does not produce fluorescent species, thus providing a deeper insight as to how enzyme activities were affected by the three-step cascade in confined spaces. Moreover, we kept the focus on maintaining the relative enzymatic concentrations constant for highlighting the versatility of our system in providing the many possible physical segregation and combinations for studying a multi-step cascade reaction.…”

Metabolic Biomarkers of Squamous Cell Carcinoma of the Aerodigestive Tract: A Systematic Review and Quality Assessment

“…All studies were Phase I biomarker discovery studies. Of the 30 included studies, 18 were from Asia and the Far East [8,9,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], seven from Europe [27][28][29][30][31][32][33], three from North America [34][35][36], and two from the Middle East [37,38]. ASCC tumour sites identified were the head and neck (n = 17), oesophageal (n = 8), and lung (n = 5).…”

“…Data preprocessing included name check against the Human Metabolome Database (HMDB), data checks, and missing values. Parameters used to analyse this data were the hypergeometric test for overrepresentation analysis and the relativebetweenness centrality test for pathway topology analysis based on the KEGG pathway library [18][19][20]. Normalisation was performed using the weighted means of identified metabolite that were increased/decreased in squamous cell carcinoma (SCC) in each sample type.…”

A Comparative Study of Topology-based Pathway Enrichment Analysis Methods

“…For each pathway, the geometric mean of all rankings across different mean changes is taken as the final measure of relative performance. Spreadsheets with disaggregated empirical powers for each pathway, across all the experimental settings, are provided in [32].…”

“…Detailed results for all parameter settings in Excel spreadsheets, together with datasets and R code for reproducing the results of this study can be found in [32].…”