Statistical Analysis and Interpretation of Metabolomics Data

Advanced analytical and statistical methods have expanded the role of metabolomics in generating systems-level biological insight, requiring careful interpretation of complex and high-dimensional data. As the final analytical module, this mini-course consolidates statistical reasoning skills developed earlier while emphasizing key differences between proteomics and metabolomics data interpretation. Students explore common univariate and multivariate analysis outputs, pathway-level summaries, and challenges associated with metabolite identification and functional annotation. Through guided dry-lab analysis of representative metabolomics datasets and discussion of case studies, learners link metabolic patterns to biological hypotheses and integrate metabolomics findings into broader experimental and biological contexts.

Registration in this course is restricted to students admitted to the Proteomics and Metabolomics Applications program.

Corequisite(s): BINF 4025, 4030, and 4035.