PCA is mostly used as a tool in exploratory data analysis and building predictive models. It is often used to visualize genetic distance and relatedness between populations. PCA is either done by singular value decomposition of a design matrix or by the following 2 steps:
|Calculating the data covariance (or correlation) matrix of the original data.|
|Performing eigenvalue decomposition on the covariance matrix.|
|Project name||PCA analysis service|
|Samples requirement||Please provide the original file after your calculation. For molecular dynamics service, please provide the initial structure of PDB ID.|
|Deliverables||We provide you with raw data and calculation result analysis service.|
|Application||A variant of principal components analysis is used in neuroscience to identify the specific properties of a stimulus that increase a neuron's probability of generating an action potential.|
CD ComputaBio provides corresponding PCA analysis services. Structure and function are the central issues of modern molecular biology. The interaction between molecules is the cornerstone of this axis problem. The interactions between molecules mainly include covalent bonds, ionic bonds, hydrogen bonds, van der Waals forces, hydrophobic interactions, etc. The CD ComputaBio team has been working in this field for more than ten years, and can provide you with accurate analysis of related forces. If you have needs in this regard, please feel free to contact us.