PCA - Creative ProteomicsPrincipal component analysis (2017)
Principal component analysis (PCA) is a broadly used statistical method that uses an orthogonal transformation to convert a set of observations of conceivably correlated variables into a set of values of linearly uncorrelated variables called principal components. This is an unsupervised statistical analysis approach that is probably the most widely used statistical tool in metabolomics studies. PCA is mostly used as a tool in exploratory data analysis and for making predictive models.
- Principal component analysis,
- creative proteomics
Publication DateWinter December 19, 2017
Citation InformationBennie Liu. "PCA - Creative Proteomics" Principal component analysis (2017)
Available at: http://works.bepress.com/Bioarray/9/