Abstract

The final workflow in the array of clinical metaproteomics tutorials is the data interpretation workflow. Interpreting MaxQuant data using MSstats involves applying a rigorous statistical framework to glean meaningful insights from quantitative proteomic datasets. The MaxQuant output is explored to understand data distribution and variability. Subsequent normalization helps account for systematic variations. MSstats allows the user to define the experimental design, including sample groups and conditions, to perform statistical analysis. The output provides valuable information about differential protein expression across conditions, estimates of fold changes, and associated p-values, aiding in the identification of biologically significant proteins. Furthermore, MSstats enables quality control and data visualization, ultimately enhancing our ability to draw meaningful conclusions from complex proteomic datasets. Additional tutorial material for using MaxQuant and MSstatTMT for TMT data analysis can be found at MaxQuant and MSstats for the analysis of TMT data.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• Why do we need to interpret the data?
• Can we visualize the data?

Learning Objectives

• Perform group comparison analysis.
• Analyze significant proteins
• Look at the taxonomic distribution of the quantified peptides