PLS-DA Analysis

What is PLS-DA analysis?

Partial Least-Squares Discriminant Analysis (PLS-DA) is a multivariate dimensionality-reduction tool that has been popular in the field of chemometrics for well over two decades, and has been recommended for use in omics data analyses. PLS-DA is gaining popularity in metabolomics and in other integrative omics analyses. Both chemometrics and omics data sets are characterized by large volume, large number of features, noise and missing data. These data sets also often have lot fewer samples than features. PLS-DA can be thought of as a "supervised" version of principal component analysis (PCA) in the sense that it achieves dimensionality reduction but with full awareness of the class labels. Besides its use for dimensionality-reduction, it can be adapted to be used for feature selection as well as for classification.

Overall solutions

PLS-DA Analysis

  • CD ComputaBio provides professional PLS-DA services, which is a supervised clustering or classification method, and a chemometric technique used to optimize the separation between different groups of samples.
  • This method is actually an extension of the PLS1 method, dealing with a single dependent variable continuous variable, while PLS2 (called PLS-DA) can handle multiple dependent variables.
  • Our service aims to maximize the covariance between the independent variable X (sample readings, metabolomics data) and the corresponding dependent variable Y (group or class) of highly multidimensional data by finding the linear subspace of the explanatory variable.
  • The new subspace allows the Y variable to be predicted based on a reduced number of factors (PLS components or so-called latent variables). These factors that span the subspace onto which the independent variable X is projected describe the behavior of the dependent variable Y.

Algorithm

Many variables of partial least squares are used to estimate the factors and loading matrices T, U, P, and Q. Most of them construct a linear regression estimate Y=XB+Bo between X and Y. Some partial least squares algorithms are only suitable for the case where Y is a column vector, while other algorithms deal with the general case where Y is a matrix. The algorithm also differs according to whether they estimate the factor matrix T as an orthogonal matrix. The final prediction is the same in all different least square algorithms, but the components are different.

Our services

Project name PLS-DA analysis
Our services
  • CD ComputaBio provides you with professional PLS-DA analysis services. The analysis results can be widely used in bioanalysis such as metabolomics.
Screening cycle Decide according to your needs.
Service including We provide you with raw data and analysis service.
Price Inquiry

CD ComputaBio' PLS-DA analysis can significantly increase the hit rate of lead compounds and reduce the cost of later experimental screening. PLS-DA 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.

* It should be noted that our service is only used for research, not for clinical use.

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