Metabolomics, Lipodomics, and Proteomics Data Analysis in R: PCA & PLS-DA with Interactive 3D Plots

In this tutorial, I demonstrate how to perform PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares Discriminant Analysis) for metabolomics data using R. You will learn how to load metabolomics peak area or normalized data from a CSV file, preprocess the dataset using log transformation and scaling, and generate 3D interactive PCA and PLS-DA plots for data exploration and group separation analysis.

This step-by-step guide is designed for students, researchers, and bioinformatics scientists working with LC-MS and Metabolomics, Lipodomics, and Proteomics datasets who want to create publication-quality visualizations and understand multivariate statistical analysis in metabolomics.

This workflow can be applied to biomarker discovery, cancer metabolomics, disease classification, QC analysis, and multi-omics data visualization.

Find the code and example dataset: https://github.com/educationassist25/pca_pls-da

Видео Metabolomics, Lipodomics, and Proteomics Data Analysis in R: PCA & PLS-DA with Interactive 3D Plots канала BioAnalytics Lab