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Proteomics Service

Multivariate Analysis Service

Multivariate analysis (MVA) is founded on the statistical principle of multivariate statistics, which involves simultaneous observation and analysis of more than two statistical outcome variables at a time. Multivariate analysis can be complex by the desire to include physics-based analysis to measure the effect of variables for a hierarchical "system-of-systems". Studies aimed at multivariate analysis are often stalled by the dimensionality of the problem. These concerns are frequently eased by using surrogate models, highly accurate approximations of the physics-based code. Since surrogate models take the form of an equation, they can be estimated very quickly. This makes it possible for large-scale MVA: while a Monte Carlo simulation across the design space is very difficult with physics-based codes, it becomes worthless when evaluating surrogate models, which frequently take the form of response-surface equations. Multivariate statistics has been a useful tool for the analysis of metabolomic data.

Multivariate Analysis Service

Because metabolomic data are usually composed of dozens of features (peaks, compounds), many of these change as a function of time, phenotype or different experimental conditions, multivariate data analysis is desired for analyzing metabolomic data. MVA includes a lot of techniques, such as PCA, multivariate ANOVA, multivariate regression analysis, factor analysis and discriminant analysis. Creative Proteomics supports multiple widely used multivariate methods such asPCA and PLS-DA. These two methods are very useful for metabolomic data analysis through dimensional reduction and data visualization.

Multivariate analysis service provided by Creative Proteomics include:

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