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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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Representative examplesof independent and dependent (paired) two-groupexperimental design. Extracted ion chromatogram and box-plot/pairedplot are shown for the features of interest. (A) A significantly down-regulated(p < 0.001) metabolite feature (m/z 171.005; METLIN MS/MS match, glycerol phosphate)in independent group design (control versus stressed bacterial population)was identified by using an independent parametric Welch t test. Welch’s t test is used to comparethe means of two independent sample groups with the assumption thattwo-group variances may differ. (B) A significantly higher level (p < 0.001) of metabolite feature (m/z 309.279; METLIN hit, eicosenoic acid) in arterial bloodplasma was determined by a paired nonparametric Wilcoxon test. Wilcoxonsigned-rank test is a nonparametric alternative to the paired t test used to compare the related samples.
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fig2: Representative examplesof independent and dependent (paired) two-groupexperimental design. Extracted ion chromatogram and box-plot/pairedplot are shown for the features of interest. (A) A significantly down-regulated(p < 0.001) metabolite feature (m/z 171.005; METLIN MS/MS match, glycerol phosphate)in independent group design (control versus stressed bacterial population)was identified by using an independent parametric Welch t test. Welch’s t test is used to comparethe means of two independent sample groups with the assumption thattwo-group variances may differ. (B) A significantly higher level (p < 0.001) of metabolite feature (m/z 309.279; METLIN hit, eicosenoic acid) in arterial bloodplasma was determined by a paired nonparametric Wilcoxon test. Wilcoxonsigned-rank test is a nonparametric alternative to the paired t test used to compare the related samples.

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)

Representative examplesof independent and dependent (paired) two-groupexperimental design. Extracted ion chromatogram and box-plot/pairedplot are shown for the features of interest. (A) A significantly down-regulated(p < 0.001) metabolite feature (m/z 171.005; METLIN MS/MS match, glycerol phosphate)in independent group design (control versus stressed bacterial population)was identified by using an independent parametric Welch t test. Welch’s t test is used to comparethe means of two independent sample groups with the assumption thattwo-group variances may differ. (B) A significantly higher level (p < 0.001) of metabolite feature (m/z 309.279; METLIN hit, eicosenoic acid) in arterial bloodplasma was determined by a paired nonparametric Wilcoxon test. Wilcoxonsigned-rank test is a nonparametric alternative to the paired t test used to compare the related samples.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: Representative examplesof independent and dependent (paired) two-groupexperimental design. Extracted ion chromatogram and box-plot/pairedplot are shown for the features of interest. (A) A significantly down-regulated(p < 0.001) metabolite feature (m/z 171.005; METLIN MS/MS match, glycerol phosphate)in independent group design (control versus stressed bacterial population)was identified by using an independent parametric Welch t test. Welch’s t test is used to comparethe means of two independent sample groups with the assumption thattwo-group variances may differ. (B) A significantly higher level (p < 0.001) of metabolite feature (m/z 309.279; METLIN hit, eicosenoic acid) in arterial bloodplasma was determined by a paired nonparametric Wilcoxon test. Wilcoxonsigned-rank test is a nonparametric alternative to the paired t test used to compare the related samples.
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