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DAVe: Data Analysis and Visualization engine (coming soon)

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Dynamic multivariatE data analysis And VIsUalization PlatforM is a browser-based multivariate data analysis solution implemented in Shiny.

MetaMapR

A metabolomic network mapping tool.

Microsoft Excel add-in for multivariate analyses and visualizations using R. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal (PCA) and independent component analyses (ICA), partial least squares regression (PLS) and discriminant analysis (PLS-DA), through an intuitive interface for creating high quality two and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks.
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Figure 1. The type 2 diabetes-associated lipidomic changes projected in context of their biological relationships in obese African-American women.

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).

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

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).

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).

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SOFTWARE

DAVe: Data Analysis and Visualization engine (coming soon)

DeviumWeb

Dynamic multivariatE data analysis And VIsUalization PlatforM is a browser-based multivariate data analysis solution implemented in Shiny.

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