Proteomics Data Analysis (PDA)

Course Description

Mass spectrometry based proteomic experiments generate ever larger datasets and, as a consequence, complex data interpretation challenges. This course focuses on the statistical concepts for peptide identification, quantification, and differential analysis. Moreover, more advanced experimental designs and blocking will also be introduced. The core focus will be on shotgun proteomics data, and quantification using label-free precursor peptide (MS1) ion intensities. The course will rely exclusively on free and userfriendly opensource tools in R/Bioconductor. The course will provide a solid basis for beginners, but will also bring new perspectives to those already familiar with standard data interpretation procedures in proteomics.

Students can sharpen their background knowledge on Mass Spectrometry, Proteomics & Bioinformatics for Proteomics here: Mass Spectrometry and Bioinformatics for Proteomics

Target Audience

This course is oriented towards biologists and bioinformaticians with a particular interest in differential analysis for quantitative proteomics.

According to the target audience of the course we either work with a graphical user interface (GUI) in a R/shiny App msqrob2gui e.g.

Proteomics Data Analysis course at the Gulbenkian institute
Proteomics Bioinformatics course of the EBI
BePA introduction: What can proteomics do for you?

or with R/markdowns scripts e.g.

Bioinformatics Summer School at UCLouvain
Statistical Genomics Course at Ghent University.

Software & Data

Note, that users who develop R/markdown scripts can access data both from the web or from disk within their scripts. So they do not need to download the data first. The msqrob2gui Shiny App only works with data that is available on disk.

More information on our tools can be found in our papers (L. J. Goeminne, Gevaert, and Clement 2016), (L. J. E. Goeminne et al. 2020) and (Sticker et al. 2020). Please refer to our work when using our tools.

Instructor

Lieven Clement, Associate Professor of Statistical Genomics, statOmics, Ghent University, Belgium

Issues

If you encounter problems related to the course material (e.g. package installation problems, bugs in the code, typos, …), please consider posting an issue on GitHub.

Detailed Program

Introduction to experimental design: Experimental Design, [PDF]
Preprocessing & Analysis of Label Free Quantitative Proteomics Experiments with Simple Designs

Lecture: Preprocessing, [PDF]
Tutorial: preprocessing
Wrap-up: Peptide-based Models[PDF]

Statistical Inference & Analysis of Experiments with Factorial Designs

Lecture: Differential Abundance Analysis, [PDF]
Tutorial: Design
Wrap-up: Blocking[PDF]

Previous Editions

Acknowledgements

A special thanks to Pedro Fernandes, Coordinator of the GTPB Bioinformatics Training Programme, Instituto Gulbenkian de Ciencia, who radically changed our view on teaching and immersed us in the teaching method that we use in this course.

License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

References

Goeminne, L. J. E., A. Sticker, L. Martens, K. Gevaert, and L. Clement. 2020. “MSqRob Takes the Missing Hurdle: Uniting Intensity- and Count-Based Proteomics.” Anal Chem 92 (9): 6278–87.

Goeminne, L. J., K. Gevaert, and L. Clement. 2016. “Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.” Mol Cell Proteomics 15 (2): 657–68.

Sticker, A., L. Goeminne, L. Martens, and L. Clement. 2020. “Robust Summarization and Inference in Proteome-wide Label-free Quantification.” Mol Cell Proteomics 19 (7): 1209–19.