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OPLS-DA Service

OPLS-DA Service Online Inquiry

CD Genomics is able to use OPLS-DA to help customers model the relationship between metabolite expression and sample class, thus enabling the prediction of sample class.

Introduction of OPLS-DA Service

Orthogonal partial least squares discriminant analysis is a regression modeling method of multiple dependent variables on independent variables, which can help researchers remove data in the independent variables that are irrelevant to the categorical variables and provide researchers with simpler and easier-to-interpret models. The OPLS-DA corrects the orthogonal transformation on the basis of partial least squares analysis to filter out the noise unrelated to the categorical information and improve the resolution and validity of the model.

The score plots and cross-validated score plots acquired by positive ionization derived from Orthogonal partial least squares discriminant analysis (OPLS-DA) models of plasma after cranberry juice or placebo consumption.Figure 1. The score plots and cross-validated score plots acquired by positive ionization derived from Orthogonal partial least squares discriminant analysis (OPLS-DA) models of plasma after cranberry juice or placebo consumption. (Zhao S, et al., 2020)

Application Field

OPLS-DA services can be used for but are not limited to the following research:

  • For modeling the relationship between metabolite expression and samples.
  • For the screening of differential metabolites in metabolomics.

Deliverables

  • Score chart of OPLS-DA.
  • S-plot of OPLS-DA.
  • The model validation permutation test plot of OPLS-DA.

OPLS-DA Service

OPLS-DA is an important tool for multi-omics studies, establishing correlations between data on histological multivariate variables and macroscopic data such as function, quality, and grade, or experimental conditions such as time and concentration, for the purposes of screening important variables, establishing evaluation criteria and examining the process.

  • We are able to reflect the degree of correlation of the data set with the target parameters through the model quality.
  • We are able to respond to the predictive power of the regression model by creating a scatter plot of actual versus predicted values.
  • We are able to filter the more influential variables from a heavy number of variables by using variable analysis tools such as VIP value, S-Plot, etc.

We are able to use OPLS-DA services to help our customers achieve high throughput data analysis in metabolomics. For questions about analysis content, project cycle, and pricing, please click online inquiry.

How It Works

CD Genomics is a professional bioinformatics service provider with years of experience in NGS and long-read sequencing (PacBio SMRT and Oxford Nanopore platforms) data, proteomics and metabolomics data analysis, integrated analysis services, database construction, and other bioinformatics solutions.

How It Works

CD Genomics has professional bioinformatics experts who have successfully provided OPLS-DA services to researchers in many different fields. Our professional skills and enthusiasm will provide you with high-quality analysis services. If you are interested in our services, please contact us for more details.

Reference

  1. Zhao S, et al. Identifying Cranberry Juice Consumers with Predictive OPLS-DA Models of Plasma Metabolome and Validation of Cranberry Juice Intake Biomarkers in a Double-Blinded, Randomized, Placebo-Controlled, Cross-Over Study. Mol Nutr Food Res. 2020 Jun; 64(11): e1901242.
* For Research Use Only. Not for use in diagnostic procedures.
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