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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 principal component analysis. AScores plot showingthe correlation between the samples (top panel) and a Loadings plotshowing the relationship between the metabolite features that relateto the sample grouping (bottom panel). The clusters represent wildtype and different mutant strains of Desulfovibrio alaskensis G20 (WT: wild type; MUT: mutant). The annotations for differentmutant strains are given in the legend of Figure 5. The user has the option to set the loadings threshold andto apply different scaling criteria.
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fig7: Interactive principal component analysis. AScores plot showingthe correlation between the samples (top panel) and a Loadings plotshowing the relationship between the metabolite features that relateto the sample grouping (bottom panel). The clusters represent wildtype and different mutant strains of Desulfovibrio alaskensis G20 (WT: wild type; MUT: mutant). The annotations for differentmutant strains are given in the legend of Figure 5. The user has the option to set the loadings threshold andto apply different scaling criteria.

Mentions: The XCMS Online platform was enhanced to implementpaired two-groupcomparisons, higher-order meta-analysis, and multiple group comparisons.Additional statistical tests were introduced, and the interactivevisualization tools (Figures 2–7) were improved and developed to help deconvolvecomplex untargeted metabolomic data sets. The statistical tests arecarried out systematically following feature detection and profilealignment, providing users an interface to directly visualize differentiallyexpressed or significantly altered metabolic features. Here we highlightthe appropriate usage of different statistical tests and demonstratethe value of interactive, univariate (cloud plot), and multivariate(PCA plots) visualization tools for different experimental designs:two-group comparison, meta-analysis, and multigroup comparison.


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 principal component analysis. AScores plot showingthe correlation between the samples (top panel) and a Loadings plotshowing the relationship between the metabolite features that relateto the sample grouping (bottom panel). The clusters represent wildtype and different mutant strains of Desulfovibrio alaskensis G20 (WT: wild type; MUT: mutant). The annotations for differentmutant strains are given in the legend of Figure 5. The user has the option to set the loadings threshold andto apply different scaling criteria.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: Interactive principal component analysis. AScores plot showingthe correlation between the samples (top panel) and a Loadings plotshowing the relationship between the metabolite features that relateto the sample grouping (bottom panel). The clusters represent wildtype and different mutant strains of Desulfovibrio alaskensis G20 (WT: wild type; MUT: mutant). The annotations for differentmutant strains are given in the legend of Figure 5. The user has the option to set the loadings threshold andto apply different scaling criteria.
Mentions: The XCMS Online platform was enhanced to implementpaired two-groupcomparisons, higher-order meta-analysis, and multiple group comparisons.Additional statistical tests were introduced, and the interactivevisualization tools (Figures 2–7) were improved and developed to help deconvolvecomplex untargeted metabolomic data sets. The statistical tests arecarried out systematically following feature detection and profilealignment, providing users an interface to directly visualize differentiallyexpressed or significantly altered metabolic features. Here we highlightthe appropriate usage of different statistical tests and demonstratethe value of interactive, univariate (cloud plot), and multivariate(PCA plots) visualization tools for different experimental designs:two-group comparison, meta-analysis, and multigroup comparison.

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