metabolites categorized by metabolic super pathway assignment  (Metabolon Inc)

Journal: Scientific Reports

Article Title: Exercise induces dynamic changes in intra-articular metabolism and inflammation associated with remodeling of the infrapatellar fat pad in mice

doi: 10.1038/s41598-025-86726-0

Figure Lengend Snippet: Effect of short-term wheel running exercise on synovial fluid metabolites. Synovial fluid was collected from knees of male and female mice following 0, 1, 3, or 5 days of voluntary wheel running exercise and intra-articular injections, as described in Fig. . Samples were analyzed by Metabolon’s Global Metabolomic Profiling Analysis, which identified 202 biochemicals confirmed by authenticated library standards. Peak area data were normalized to extracted volume and then median scaled. ( A ) 2-way hierarchical clustering analysis was used to identify patterns in synovial fluid metabolite abundance across experimental groups. Heatmap color legend signifies standardized metabolite values calculated by subtracting the mean and dividing by the standard deviation. Columns represent mean values per experimental group, and rows represent individual metabolites. Note that 3- and 5-day exercise conditions clustered together in the third and fourth columns. Filled cells in right-hand column indicate metabolites significantly altered by exercise (Generalized Linear Model including exercise, sex, and treatment effects). Green rectangles designate clusters (C1 – C5) with distinct changes in metabolite abundance versus days of exercise. ( B ) Pie chart of relative percent of all detected metabolites categorized by Metabolon’s Metabolic Super Pathway assignment. Numbers in parentheses indicate absolute number of metabolites detected per category. ( C ) Pie charts of cluster-specific metabolite composition based on Metabolic Super Pathway assignments. Donut charts indicate the relative proportion of metabolites within a given cluster that were significantly altered by exercise, biological sex, or treatment ( p < 0.05). Line graphs of metabolites significantly altered by exercise, expressed as abundance fold-change relative to day 0 values (log2) and color coded according to Metabolic Super Pathway (Supplemental Table 7).

Article Snippet: Green rectangles designate clusters (C1 – C5) with distinct changes in metabolite abundance versus days of exercise. ( B ) Pie chart of relative percent of all detected metabolites categorized by Metabolon’s Metabolic Super Pathway assignment.

Techniques: Standard Deviation

Journal: Metabolomics

Article Title: Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

doi: 10.1007/s11306-018-1420-2

Figure Lengend Snippet: Overall amounts of missing data and LOD effects. a, b The overall fraction of missing values across metabolites and observations, respectively. c, d Scatter plots and boxplots of selected metabolite pairs to illustrate missing data due to LOD and non-LOD effects, respectively. Blue—observed concentrations. Red—observed values of the auxiliary metabolite in observations with missing values of the investigated metabolite. Note that red data points are not part of the x-axis but were plotted in the same scatterplot for clarity. corr correlation, p p-value of correlation, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p_{Wst}}~$$\end{document} p W s t = p-value of Wilcoxon–Mann–Whitney test

Article Snippet: Subsequently, we used a priori pathway annotations from Metabolon Inc., where each metabolite was assigned to one pathway (e.g., branched-chain amino acids, lysolipids, xanthines) to calculate pathway-based modularity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(Q),$$\end{document} ( Q ) , according to (Newman and Girvan ; Krumsiek et al. ).

Techniques: MANN-WHITNEY

Journal: Metabolomics

Article Title: Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

doi: 10.1007/s11306-018-1420-2

Figure Lengend Snippet: Run day-dependent effects on missing data. a Normalized amount of missing values per run day in each platform (LC/MS+, LC/MS−, GC/MS). For a given metabolite and run day, the normalized amount of missing data per run day was calculated as the number of missing values for the respective metabolite on the respective run day divided by the total number of observations for that run day, divided by the median amount of missing data of that metabolite over all run days. Thus, a normalized run day-missingness of 1 is the average run day-missingness for a given metabolite. Pearson correlation coefficients were calculated across all pairs of platforms. b Standard deviation of missing values across run days, depending on the total amount of missing data for each platform. Each dot in the plot shows the total proportion of missing values and the run day variation for one metabolite. c, d The distribution of the total amount of missing values is shown for a metabolite with moderate (ursodeoxycholate) and high (gamma-glutamylisoleucine) standard deviation

Article Snippet: Subsequently, we used a priori pathway annotations from Metabolon Inc., where each metabolite was assigned to one pathway (e.g., branched-chain amino acids, lysolipids, xanthines) to calculate pathway-based modularity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(Q),$$\end{document} ( Q ) , according to (Newman and Girvan ; Krumsiek et al. ).

Techniques: Liquid Chromatography with Mass Spectroscopy, Gas Chromatography-Mass Spectrometry, Standard Deviation

Journal: Metabolomics

Article Title: Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

doi: 10.1007/s11306-018-1420-2

Figure Lengend Snippet: Evaluation of imputation approaches on real data. a Pathway-based modularity for each imputation strategy. Modularity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q$$\end{document} Q was calculated based on pathways. Vertical lines represent bootstrap-based confidence intervals (1000 times resampling). b The ability to gain statistical power and to preserve real metabolite-SNP associations after imputation. Circle color represents the ability of imputation methods to preserve effect sizes, with red and blue indicating possible overestimation and underestimation, respectively, and yellow corresponding to cases with good preservation of the association. Circle size depicts the gain in statistical power after imputation. The bigger the circle the higher the statistical power gain after imputation compared to CCA . Squares correspond to cases where no statistical power was gained. Note that due to readability issues, only KNN-based imputation methods with K = 3, 10, and 20 were included, whereas KNN imputation with K = 1 and 5 can be found in File S6 and Table S8

Article Snippet: Subsequently, we used a priori pathway annotations from Metabolon Inc., where each metabolite was assigned to one pathway (e.g., branched-chain amino acids, lysolipids, xanthines) to calculate pathway-based modularity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(Q),$$\end{document} ( Q ) , according to (Newman and Girvan ; Krumsiek et al. ).

Techniques: Preserving