Executive Development Programme in Strategic Metabolomics Data Analysis

• Introduction to Metabolomics Data Analysis: Overview of metabolomics data analysis, including primary techniques and tools used in the field.
• Data Preprocessing and Quality Control: Techniques for cleaning and preparing metabolomics data for analysis, including quality control methods.
• Multivariate Data Analysis in Metabolomics: Introduction to multivariate data analysis methods commonly used in metabolomics, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).
• Statistical Analysis of Metabolomics Data: Overview of statistical analysis methods for metabolomics data, including hypothesis testing and multiple testing correction.
• Pathway Analysis in Metabolomics: Introduction to pathway analysis methods commonly used in metabolomics, including over-representation analysis and gene set enrichment analysis (GSEA).
• Integration of Metabolomics Data with Other Omics Data: Techniques for integrating metabolomics data with other omics data, including genomic, transcriptomic, and proteomic data.
• Machine Learning in Metabolomics Data Analysis: Overview of machine learning methods for metabolomics data analysis, including supervised and unsupervised learning.
• Data Visualization in Metabolomics: Techniques for visualizing metabolomics data, including heatmaps, volcano plots, and network diagrams.
• Clinical Applications of Metabolomics Data Analysis: Real-world examples of how metabolomics data analysis is used in clinical settings, including disease diagnosis and biomarker discovery.