Statistical Analysis and Interpretation of Proteomics Data

High-throughput proteomics have shifted the focus of omics research from data generation to data interpretation, increasing the demand for professionals skilled in statistical reasoning and biological inference. Students explore core analytical concepts used in proteomics data analysis, including normalization, exploratory data analysis, differential protein expression, and pathway enrichment. Through dry-lab analysis of representative proteomics datasets, learners gain experience interpreting common visualizations and assessing statistical and biological significance. By working with simplified analytical pipelines and engaging with real-world proteomics examples, students develop foundational skills for clearly communicating results and deriving biologically meaningful insights from protein-level data.

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

Corequisite(s): BINF 4025, 4030, and 4040.