2014 Metabolomic Data Analysis and Visualization Workshop and Tutorials | Creative Data Solutions

2014 Metabolomic Data Analysis and Visualization Workshop and Tutorials

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Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.

Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs including use cases for the freely available data analysis software listed below.

You can check out the introduction lecture to the covered material below.

New additions to the course include lecture and lab on Data normalization and updated and improved software.

Software

DeviumWeb (new features)

MetaMapR (new features)

Stay tuned for videos of all of the material!

2014 Metabolomics Data Analysis and Visualization Tutorials Dmitry Grapov is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This entry was posted on October 11, 2014 by dgrapov. It was filed under Uncategorized and was tagged with biochemical network, data analysis, data visualization, demo, DeviumWeb, lectures, metabolomics, MetaMapR, r-bloggers, tutorial, west coast metabolomics center, workshop.

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imDEV clouds

loess_norm50

Cancer tissue network

Metabolites are represented by circular “nodes” linked by “edges” with arrows designating the direction of the biosynthetic gradient (i.e. substrate to product). Some metabolites are linked by more than one enzymatic step. Node sizes represent magnitudes of differences in plasma metabolite geometric means (ΔGM). Arrow widths represent magnitudes of changes in product over substrate ratios (ΔP:S). Colors of node borders and arrows represent the significance and direction of changes relative to non-diabetics as per the figure legend. Differences are significant at p<0.05 by Mann-Whitney U test adjusted for FDR (q = 0.1).

Metabolites are represented by circular “nodes” linked by “edges” with arrows designating the direction of the biosynthetic gradient (i.e. substrate to product). Some metabolites are linked by more than one enzymatic step. Node sizes represent magnitudes of differences in plasma metabolite geometric means (ΔGM). Arrow widths represent magnitudes of changes in product over substrate ratios (ΔP:S). Colors of node borders and arrows represent the significance and direction of changes relative to non-diabetics as per the figure legend. Differences are significant at p<0.05 by Mann-Whitney U test adjusted for FDR (q = 0.1).

Bionetwork1

ASMS 2014 j fahrman

OPLS-DA network

333

Treatment response network

cough syrup network

known partial correlation network2

mc 2

journal.pone.0048852.g001

journal.pone.0048852.g002

LOESS_span

network

genotype network

PLS_DA repeated measures trajectory

C and E figure

Tissue network cancer

Spearman’s correlations were used to generate multi-dimensionally scaled parameter connectivity networks for variable intercorrelations. Networks were oriented with fasting glucose at the origin and SFA in the lower right quadrant. Colored ellipses represent the 95% probability locations of metabolite classes (Hoettlings T2, p<0.05). Nodes indicate clinical parameters (diamonds), <20-carbon fatty acid metabolites (circles) and ≥20-carbon fatty acid metabolites (triangles), with discriminant model variables and glucose enlarged. Significant correlations between species are designated by orange (positive) or blue (negative) connecting lines (p<0.05, non-diabetic; p<0.01, diabetic participants).

Spearman’s correlations were used to generate multi-dimensionally scaled parameter connectivity networks for variable intercorrelations. Networks were oriented with fasting glucose at the origin and SFA in the lower right quadrant. Colored ellipses represent the 95% probability locations of metabolite classes (Hoettlings T2, p<0.05). Nodes indicate clinical parameters (diamonds), <20-carbon fatty acid metabolites (circles) and ≥20-carbon fatty acid metabolites (triangles), with discriminant model variables and glucose enlarged. Significant correlations between species are designated by orange (positive) or blue (negative) connecting lines (p<0.05, non-diabetic; p<0.01, diabetic participants).

network_1

network

composite2

network_1

Treatment effects network

Horizontal scatter plots of the log transformed concentrations for each model variable are shown. The horizontal arrangement of metabolite scatter plots is scaled to their loading in the discriminant model. A given species importance in the classification increases with increasing displacement from the origin (broken line). The direction of the displacement, left or right, designates whether the species was decreased (left) or increased (right) in the diabetic relative to the non-diabetic patients. The overall model discrimination performance is presented as a scatter plot of subject model scores (inset).

Horizontal scatter plots of the log transformed concentrations for each model variable are shown. The horizontal arrangement of metabolite scatter plots is scaled to their loading in the discriminant model. A given species importance in the classification increases with increasing displacement from the origin (broken line). The direction of the displacement, left or right, designates whether the species was decreased (left) or increased (right) in the diabetic relative to the non-diabetic patients. The overall model discrimination performance is presented as a scatter plot of subject model scores (inset).

WCMC network

PCA normalizations

Figure 1. The type 2 diabetes-associated lipidomic changes projected in context of their biological relationships in obese African-American women.

Figure 1. The type 2 diabetes-associated lipidomic changes projected in context of their biological relationships in obese African-American women.

Scatterplot matrix for overview of correlations and regressions, displaying box plots for Iris data species, variable histograms, correlation statistics, stripcharts and best fit lines.

Scatterplot matrix for overview of correlations and regressions, displaying box plots for Iris data species, variable histograms, correlation statistics, stripcharts and best fit lines.

PLS-DA NETWORK

netmaping

g9135

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