Fig 1.
Over Representation Analysis (ORA).
Venn diagram representing ORA parameters corresponding to Eq 1. N represents compounds forming the background set, which covers part of the full metabolome. M represents compounds in the pathway of interest. n represents compounds of interest (i.e., differentially abundant metabolites), and k represents the overlap between the list of compounds of interest and compounds in the pathway.
Table 1.
Summary of experimental datasets used in this work.
An asterisk (*) besides the MS platform indicates no chromatography/electrophoresis was used in the assay.
Fig 2.
A) Scatter plot of -log10 p-values of pathways when using an assay-specific background set consisting of all measurable compounds in each dataset (x-axis) compared to using a non-specific background set containing all compounds mapping to at least one KEGG pathway (y-axis). Dashed black lines represent a p-value threshold equivalent to p = 0.1. Regression lines are shown with shading representing the 95% confidence interval. B) Number of pathways significant at p ≤ 0.1 (solid bars) and the number of pathways significant at q < 0.1 (hashed bars, BH FDR correction). Datasets are ordered by number of compounds mapping to KEGG pathways. C and D) The effect of reducing the size of the background set. C) Compounds were removed from the background set at random and DA metabolites were identified based on the modified background set. D) Only non-DA compounds were removed from the background set at random. In all panels a, c & d, dashed lines represent datasets where no chromatography/electrophoresis was used. Error bars represent standard error of the mean.
Fig 3.
The effect of the number of DA metabolites in the list of metabolites of interest on the number of significant pathways (p ≤ 0.1) in the Labbé et al. dataset. Results corresponding to Bonferroni thresholds are denoted by red markers while those corresponding to BH FDR thresholds are denoted by black markers. Marker shape (circle, cross, or triangle) represents the adjusted p-value threshold for DA metabolite selection (0.005, 0.05, and 0.1 respectively).
Fig 4.
Comparison of pathway databases and database updates.
A) Pathway size distribution of KEGG, Reactome, and HumanCyc databases. Violin plots show the distribution of pathway size (number of compounds, log10 transformed). Bold vertical lines show median, dashed vertical lines show lower and upper quartiles. B) Comparison of Reactome human pathway set (R-HSA) releases spanning the years 2017 (R61, June 2017) to 2020 (R75, December 2020). Data for release 67 was not available. Dot colour corresponds to release version, with lighter colours representing newer releases.
Table 2.
Organism-specific vs. reference pathways.
Number of SEP (P ≤ 0.1) detected in both the KEGG organism-specific and KEGG reference pathway sets, and those significant in only one of the sets.
Fig 5.
The effect of compound misidentification by molecular weight (20ppm window) (bars in dark colours) and chemical formula (bars in light colours) on the mean pathway loss rate (lower bars) and mean pathway gain rate (upper bars) averaged over 100 random resamplings at 4% misidentification. Error bars represent standard error of the mean.
Fig 6.
The effect of assay chemical specificity on pathways accessible in the KEGG metabolic network.
Both figures a and b are based on the four assay types present in the Stevens et al. dataset. The colours in each subfigure correspond to the four assay types shown in the legend. A) KEGG reference metabolic network with compounds from each assay type highlighted on their respective pathways. KEGG network annotated using iPath 3 [22]. B) Venn diagram showing the number of KEGG pathways accessible using the compounds in each of the four assay types. Numbers outside the Venn diagram indicate the total number of pathways accessible with each assay type. Venn created using InteractiVenn [23].