Multivariate analysis (MVA) is based on the principles of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time. Typically, MVA is used to address the situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important. A modern, overlapping categorization of MVA includes:
Principal component analysis (PCA) is a widely used statistical method that uses orthogonal transformation to convert a set of imaginable observations of related variables into a set of values of linear unrelated variables, called principal components. The PCA provided by CD ComputaBio is mainly used as a tool for exploratory data analysis and building predictive models.
Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most widely used classification techniques in chemical indicators. The PLS-DA service provided by CD ComputaBio has been widely used in metabolomics, proteomics and genomics.
CD ComputaBio introduced orthogonal partial least squares discriminant analysis (OPLS-DA) as an improvement to the PLS-DA method that uses multivariate data to distinguish two or more groups (classes). We are willing to provide you with OPLS-DA service!
|Project name||Multivariate analysis|
|Screening cycle||Decide according to your needs.|
|Deliverables||We provide you with raw data and analysis service.|
CD ComputaBio' multivariate analysis can significantly reduce the cost and labor of the subsequent experiments. Multivariate analysis is a personalized and customized innovative scientific research service. Each project needs to be evaluated before the corresponding analysis plan and price can be determined. If you want to know more about service prices or technical details, please feel free to contact us.