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Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses.

Gowda H, Ivanisevic J, Johnson CH, Kurczy ME, Benton HP, Rinehart D, Nguyen T, Ray J, Kuehl J, Arevalo B, Westenskow PD, Wang J, Arkin AP, Deutschbauer AM, Patti GJ, Siuzdak G - Anal. Chem. (2014)

Bottom Line: On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity.The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots.The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria.

View Article: PubMed Central - PubMed

Affiliation: Scripps Center for Metabolomics and Mass Spectrometry and ‡Department of Cell Biology, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States.

ABSTRACT
XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

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Related in: MedlinePlus

Interactive multigroup cloud plot withcustomized metabolomic datavisualization. Metabolite features whose level varies significantly(p < 0.01) across wild-type and different mutantsare projected on the cloud plot depending on their retention time(x-axis) and m/z (y-axis). Each metabolite feature is representedby a bubble. Statistical significance (p-value) is represented bythe bubble’s color intensity. The size of the bubble denotesfeature intensity. When the user scrolls the mouse over a bubble,feature assignments are displayed in a pop-up window (m/z, RT, p-value, fold change).When a bubble is selected by a “mouse click”, the EIC,Box-Whisker plot, Posthoc, and METLIN hits appear on the main panel.Each bubble is linked to the METLIN database to provide putative identificationsbased on accurate m/z. The variationpattern of glutamic acid (m/z 146.0468,MS/MS METLIN match) across different mutants is shown by a box–whiskerplot.
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fig6: Interactive multigroup cloud plot withcustomized metabolomic datavisualization. Metabolite features whose level varies significantly(p < 0.01) across wild-type and different mutantsare projected on the cloud plot depending on their retention time(x-axis) and m/z (y-axis). Each metabolite feature is representedby a bubble. Statistical significance (p-value) is represented bythe bubble’s color intensity. The size of the bubble denotesfeature intensity. When the user scrolls the mouse over a bubble,feature assignments are displayed in a pop-up window (m/z, RT, p-value, fold change).When a bubble is selected by a “mouse click”, the EIC,Box-Whisker plot, Posthoc, and METLIN hits appear on the main panel.Each bubble is linked to the METLIN database to provide putative identificationsbased on accurate m/z. The variationpattern of glutamic acid (m/z 146.0468,MS/MS METLIN match) across different mutants is shown by a box–whiskerplot.

Mentions: To visualize the statisticallysignificant results of multigroupanalysis and filter out the features of interest, the original two-groupcloud plot18 has been adapted for a multigroupoutput. Multigroup cloud plots display the metabolite features whoselevel varies significantly across different analyzed groups or dataclasses. Metabolite features are projected in the same manner as ontwo-group cloud plots, depending on their m/z ratio and retention time. The new dynamic interface enablesusers to adjust or determine the statistical significance threshold(ANOVA or Kruskal–Wallis p-value), featureintensity, m/z, and retention-timerange for the best representation of targeted features of interest.The box–whisker plot, EIC, post-HOC values, and METLIN hitscan be visualized for each metabolic feature with a simple “mouseclick” on the specific bubble. The same example of wild typeand different mutants of Desulfovibrio alaskensis G20 exposed to salt stress that was processed by the meta-analysistools was used to demonstrate the multigroup comparison. Relativeto meta-analysis where the objective was to identify the shared patternof metabolic response to stress, the multigroup analysis highlightedthe differences in the pattern of stress response across wild typeand different, hypersensitive mutants. Among many differentially expressedfeatures, the variation pattern of glutamic acid across defined wild-typeand mutant groups is shown in Figure 6. During the exposure to salt stress, the uptakeand/or synthesis of glutamic acid was significantly up-regulated inthe lysine-aminomutase enzyme mutant (MUT-34A9) when compared to theother mutants and wild type. Multigroup analysis can be essentialto discriminate the metabolic response associated with a specificphenotype and therefore to link specific metabolites with distinctfunctional roles. For example, multigroup comparison could be usedto functionally characterize different brain regions or to identifymetabolic patterns specific to different types of cancer.


Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses.

Gowda H, Ivanisevic J, Johnson CH, Kurczy ME, Benton HP, Rinehart D, Nguyen T, Ray J, Kuehl J, Arevalo B, Westenskow PD, Wang J, Arkin AP, Deutschbauer AM, Patti GJ, Siuzdak G - Anal. Chem. (2014)

Interactive multigroup cloud plot withcustomized metabolomic datavisualization. Metabolite features whose level varies significantly(p < 0.01) across wild-type and different mutantsare projected on the cloud plot depending on their retention time(x-axis) and m/z (y-axis). Each metabolite feature is representedby a bubble. Statistical significance (p-value) is represented bythe bubble’s color intensity. The size of the bubble denotesfeature intensity. When the user scrolls the mouse over a bubble,feature assignments are displayed in a pop-up window (m/z, RT, p-value, fold change).When a bubble is selected by a “mouse click”, the EIC,Box-Whisker plot, Posthoc, and METLIN hits appear on the main panel.Each bubble is linked to the METLIN database to provide putative identificationsbased on accurate m/z. The variationpattern of glutamic acid (m/z 146.0468,MS/MS METLIN match) across different mutants is shown by a box–whiskerplot.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215863&req=5

fig6: Interactive multigroup cloud plot withcustomized metabolomic datavisualization. Metabolite features whose level varies significantly(p < 0.01) across wild-type and different mutantsare projected on the cloud plot depending on their retention time(x-axis) and m/z (y-axis). Each metabolite feature is representedby a bubble. Statistical significance (p-value) is represented bythe bubble’s color intensity. The size of the bubble denotesfeature intensity. When the user scrolls the mouse over a bubble,feature assignments are displayed in a pop-up window (m/z, RT, p-value, fold change).When a bubble is selected by a “mouse click”, the EIC,Box-Whisker plot, Posthoc, and METLIN hits appear on the main panel.Each bubble is linked to the METLIN database to provide putative identificationsbased on accurate m/z. The variationpattern of glutamic acid (m/z 146.0468,MS/MS METLIN match) across different mutants is shown by a box–whiskerplot.
Mentions: To visualize the statisticallysignificant results of multigroupanalysis and filter out the features of interest, the original two-groupcloud plot18 has been adapted for a multigroupoutput. Multigroup cloud plots display the metabolite features whoselevel varies significantly across different analyzed groups or dataclasses. Metabolite features are projected in the same manner as ontwo-group cloud plots, depending on their m/z ratio and retention time. The new dynamic interface enablesusers to adjust or determine the statistical significance threshold(ANOVA or Kruskal–Wallis p-value), featureintensity, m/z, and retention-timerange for the best representation of targeted features of interest.The box–whisker plot, EIC, post-HOC values, and METLIN hitscan be visualized for each metabolic feature with a simple “mouseclick” on the specific bubble. The same example of wild typeand different mutants of Desulfovibrio alaskensis G20 exposed to salt stress that was processed by the meta-analysistools was used to demonstrate the multigroup comparison. Relativeto meta-analysis where the objective was to identify the shared patternof metabolic response to stress, the multigroup analysis highlightedthe differences in the pattern of stress response across wild typeand different, hypersensitive mutants. Among many differentially expressedfeatures, the variation pattern of glutamic acid across defined wild-typeand mutant groups is shown in Figure 6. During the exposure to salt stress, the uptakeand/or synthesis of glutamic acid was significantly up-regulated inthe lysine-aminomutase enzyme mutant (MUT-34A9) when compared to theother mutants and wild type. Multigroup analysis can be essentialto discriminate the metabolic response associated with a specificphenotype and therefore to link specific metabolites with distinctfunctional roles. For example, multigroup comparison could be usedto functionally characterize different brain regions or to identifymetabolic patterns specific to different types of cancer.

Bottom Line: On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity.The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots.The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria.

View Article: PubMed Central - PubMed

Affiliation: Scripps Center for Metabolomics and Mass Spectrometry and ‡Department of Cell Biology, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States.

ABSTRACT
XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

Show MeSH
Related in: MedlinePlus