Principal Component Analysis

Principal component analysis is a multivariate statistical method that examines the correlation between multiple variables. It studies how to reveal the internal structure of multiple variables through a few principal components, that is, to derive a few principal components from the original variables and make them Keep as much information of the original variables as possible, and they are not related to each other. Usually the mathematical processing is to make a linear combination of the original P indicators as a new comprehensive indicator.

Figure 1. Principal component analysis PCA of a multivariate Gaussian distribution

Principle

When using statistical analysis methods to study multivariate topics, too many variables will increase the complexity of the topic. People naturally hope that there are fewer variables and more information. In many cases, there is a certain correlation between variables. When there is a certain correlation between two variables, it can be explained that the two variables reflect the information of this subject to a certain degree of overlap. Principal component analysis is to delete the redundant variables (closely related variables) for all the variables originally proposed, and establish as few new variables as possible, so that these new variables are pairwise unrelated, and these new variables are reflecting Keep the original information as much as possible in the information aspect of the subject. Try to recombine the original variables into a new set of several unrelated comprehensive variables, and at the same time, according to actual needs, a few fewer comprehensive variables can be taken out of them to reflect as much information of the original variables as possible. The statistical method is called principal component analysis or so called. Principal component analysis is also a method used in mathematics to reduce dimensionality.

Overall solutions

The principal component analysis provided by CD ComputaBio can reduce the dimensionality of the data space under study.
CD ComputaBio provides you with the principal component analysis service. By drawing the conclusion of factor load aij, you can clarify some relationships between X variables.
Graphic representation of our multidimensional data, and regression models can be constructed.
The systems we can analyze include but not limited to：

• DNA-small molecule interaction system
• RNA-small molecule interaction system
• Protein-protein interaction system

Our Features

• Principal component analysis can reduce the dimensionality of the data space under study.
• Sometimes the conclusion of factor loading a_ij can be used to clarify some relationships between X variables.
• Using principal component analysis to screen regression variables. The choice of regression variables has important practical significance. In order to make the model itself easy for structural analysis, control and forecasting, the best variable can be selected from the sub-set formed by the original variables to form the best set of variables. If you have any needs in this regard, please feel free to contact us.