Multivariate Statistics PCA, PLS-DA & Validation — Hands-on

Learn how to apply multivariate statistics to metabolomics and lipidomics datasets in a rigorous and reproducible way. You will implement PCA and PLS-DA with appropriate scaling, assess model quality, guard against overfitting, and generate publication-grade scores, loadings and validation plots.

Multivariate Statistics PCA, PLS-DA & Validation

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Session Index

Session 1 — Data Matrices, Scaling & PCA Foundations Session 2 — Supervised Models: PLS-DA & Class Separation Session 3 — Model Validation, Overfitting & Performance Metrics Session 4 — Interpretation, Reporting & Good Practices

Session 1

Fee: Rs 8800

Data Matrices, Scaling & PCA Foundations

From feature tables to X matrices

samples versus variables handling missing values log transforms and centering

Scaling strategies for metabolomics data

unit variance and Pareto range scaling and autoscaling impact of scaling on models

PCA concepts and basic diagnostics

variance explained by components scores and loadings interpretation detecting outliers and batch trends

Session 2

Fee: Rs 11800

Supervised Models: PLS-DA & Class Separation

From PCA to supervised projections

why and when to use PLS-DA encoding class labels (Y matrix) latent variables intuition

Building PLS-DA models step by step

train and test split concepts choosing number of components scores plots for group separation

Variable influence metrics and importance

VIP style measures intuition loadings and contribution plots linking features back to biology

Session 3

Fee: Rs 14800

Model Validation, Overfitting & Performance Metrics

Internal validation and resampling schemes

k-fold and repeated CV leave one out concepts stratification and class balance

Permutation tests and assessing overfitting risk

Y randomization intuition interpreting permutation plots guardrails against optimistic bias

Classification and regression performance metrics

accuracy, sensitivity, specificity ROC curves and AUC R2 and Q2 style metrics

Session 4

Fee: Rs 18800

Interpretation, Reporting & Good Practices

Reading scores, loadings and contribution plots

linking patterns to sample groups identifying influential features avoiding overinterpretation of noise

Best practices for figures and tables in metabolomics

PCA and PLS-DA scores plots feature importance and heatmaps including validation plots and metrics

Reproducible analysis and documentation checklists

recording preprocessing and scaling saving model objects and seeds sharing code, data and reports

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Multivariate Statistics PCA, PLS-DA & Validation Training | Chemometrics for Metabolomics

Multivariate Statistics PCA, PLS-DA & Validation — Hands-on