Composite score analysis for unsupervised comparison and network visualization of metabolomics data.

ABSTRACT: Metabolomics-based approaches are becoming increasingly popular to interrogate the chemical basis for phenotypic differences in biological systems. Successful metabolomics studies employ multivariate data analysis to compare large and highly complex datasets. A primary tool for unsupervised statistical analyses, principal component analysis (PCA), relies on the selection of a subsection of a maximum of three components from a larger model to visually represent similarity. The use of only three principal components limits the comprehensiveness of the model and can mask discrimination between samples. We have developed a new statistical metric, the composite score (CS), as a univariate statistic that incorporates multiple principal components to calculate a correlation matrix that enables qu

SUBMITTER: Kellogg JJ 

PROVIDER: S-EPMC6948848 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature