Chemometrics & Machine Learning for Metabolomics — Hands-on

Take your metabolomics statistics beyond basic PCA and PLS-DA. This module focuses on chemometric thinking and practical machine learning workflows for metabolomics and lipidomics: feature engineering, supervised and non linear models, robust validation and interpretation so that you can build defensible classifiers and prediction models for real research questions.

Chemometrics & Machine Learning for Metabolomics

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Session Index

Session 1 — Chemometric Thinking & Feature Engineering Session 2 — Supervised Models & Performance Assessment Session 3 — Non Linear Models, Robustness & Interpretation Session 4 — Mini Capstone: Metabolomics ML Workflow & Report

Session 1

Fee: Rs 8800

Chemometric Thinking & Feature Engineering

Chemometric view of metabolomics data matrices

samples vs variables perspective blocks, batches and design structure unsupervised vs supervised goals

Pre processing and scaling choices for ML workflows

log, Pareto, unit variance concepts centered vs uncentered data impact of scaling on models

Feature engineering and basic feature filtering strategies

variance and missingness filters biologically motivated groupings simple ratios and indices (concepts)

Session 2

Fee: Rs 11800

Supervised Models & Performance Assessment

Supervised learning tasks in metabolomics (concepts)

classification vs regression examples multiclass and ordinal setups balanced vs imbalanced designs

PCA, PLS, PLS DA and regularised linear models (high level)

chemometric roots of PLS Lasso, Ridge and Elastic Net ideas when simple models are enough

Cross validation, test sets and basic performance metrics

k fold and repeated CV concepts ROC AUC, accuracy, RMSE avoiding optimistic bias

Session 3

Fee: Rs 14800

Non Linear Models, Robustness & Interpretation

Tree based and other non linear model concepts

Random Forest and gradient boosting ideas support vector machines (high level) pros and cons for metabolomics

Model robustness, leakage checks and repeated CV thinking

feature selection within CV concept permutation style sanity checks stability of feature importance

Interpreting models and presenting results to biologists

variable importance ranking ideas partial dependence concept link to pathways and hypotheses

Session 4

Fee: Rs 18800

Mini Capstone: Metabolomics ML Workflow & Report

Designing an end to end ML workflow for a toy dataset

Theory + Practical

Comparing a simple linear model with a non linear alternative

performance vs interpretability stability across CV runs practical recommendation for users

Deliverables: ML workflow summary & result bundle

workflow outline (PDF/HTML) metrics and feature importance table (CSV/TSV) short interpretation note for collaborators

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Chemometrics & Machine Learning for Metabolomics Training | Advanced Models & Workflows

Chemometrics & Machine Learning for Metabolomics — Hands-on