Metabolomics FAQ

• • What Does a Negative Q² Value Indicate in 2D Score Plots of PLS-DA/OPLS-DA?

  In the two-dimensional score plots generated by PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis), the Q² value (predictive squared correlation coefficient) evaluates the model’s predictive performance based on cross-validation. Ideally, a Q² value close to 1 indicates strong predictive ability. A negative Q² value implies inadequate predictive performance, typically suggesting that the model fails to capture the systematic variation....

• • What Could Be the Causes of Failure to Generate 2D Score Plots in PLS-DA/OPLS-DA Analyses?

  If you encounter difficulties in generating 2D score plots from PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal PLS-DA), several potential factors should be considered: Data Preprocessing Verify that the data have undergone appropriate preprocessing steps. These may include normalization, mean-centering, and scaling, which are essential for model convergence and interpretability. Model ParametersEnsure that appropriate model parameters are specified for PLS-DA or OPLS-DA.........

• • Which Parameters Should Be Adjusted When the Q² Value in PLS-DA/OPLS-DA 2D Plots Is Negative?

  A negative Q² value in PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) typically indicates poor predictive performance. The Q² metric, derived from cross-validation, is employed to assess the model's ability to predict new data. A negative Q² implies that the model performs worse than a null or baseline model, suggesting significant issues with model generalizability. To enhance the predictive performance, the following parameters may.

• • Q&A of Untargeted Metabolomics Mass Spectrometry Analysis

  Explore key FAQs in untargeted metabolomics, from freeze-thaw effects and ionization modes to metabolite identification challenges, internal vs. external standards, and GC-MS vs. LC-MS suitability. This comprehensive guide helps researchers understand how to improve data quality and interpret biological significance in metabolomics studies.

• • Where to Find the Specific Functions of Metabolites, Such as Their Impact on Plasma Membrane Integrity

  To investigate the specific functions of metabolites, including their potential influence on cellular processes such as plasma membrane integrity, the following databases are valuable resources: 1. KEGG Pathway Database (KEGG) A comprehensive database that provides detailed information on genes, chemical compounds, enzymatic reactions, and biological pathways. The KEGG pathway module is particularly useful for elucidating the roles of metabolites within cellular metabolism and biochemical networks.

• • Is It Better for Each R²X and R²Y to Exceed 0.5 or for Their Cumulative Values? How to Interpret the Q² Intercept

  For the two-dimensional plots of PLS-DA or OPLS-DA, it is generally considered more favorable when the cumulative values of R²X and R²Y across all principal components exceed 0.5, indicating that the model explains more than half of the total variance. Specifically, R²X quantifies the proportion of variance in the predictor variables (X) that is explained by the model, while R²Y quantifies the model’s ability to account for variance in the response variables (Y). A cumulative value above 0.5 suggests that..

• • What Is the Best Method for Metabolite Detection

  Metabolite detection is commonly performed using the following methods: 1. Mass Spectrometry (MS) Mass spectrometry enables accurate determination of both the mass and structural features of metabolites. Commonly used techniques include Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS). 2. Nuclear Magnetic Resonance (NMR) NMR provides detailed structural information about metabolites and is particularly suitable for analyzing complex biological samples ........

• • How is the Molecular Weight of Polysaccharides Calculated? How is the Standard Curve for Dextran Standards Prepared?

  Calculation of Polysaccharide Molecular Weight 1.  Gel Permeation Chromatography (GPC) or High-Performance Liquid Chromatography (HPLC) These are common methods for determining the molecular weight of polysaccharides. The molecular weight is calculated by comparing the sample to standards of known molecular weight, using the sample's retention volume in the chromatographic column.   2. Light Scattering Method This method utilizes the scattering of laser light by proteins or polysaccharides in solution......

• • Is Secondary Identification Required in Untargeted Metabolomics? Why Use m/z Ratios for Metabolite Identification?

  Untargeted metabolomics typically requires secondary identification, as primary mass spectrometry data (such as m/z ratios) usually only provide information on the ion's mass-to-charge ratio and potential molecular formulas, but cannot accurately determine the compound's structure. Secondary identification, through tandem mass spectrometry analysis, provides additional structural information about metabolites, which helps ensure the accuracy of compound identification.   However, in certain cases, res......

• • How Much Internal Standard Should Be Added in Targeted Metabolomics?

  The amount of internal standard added in targeted metabolomics analysis depends on several factors, including sample type, analytical method, instrument sensitivity, and the concentration range of the target metabolites. Below are some general guidelines:   Selection of the Appropriate Internal Standard 1. Isotopically Labeled Internal Standards These are internal standards labeled with isotopes that have a similar structure to the target metabolites but differ in mass.   2. Structurally Similar Inter......