Expression Proteomics Analysis in R
About the course | Intended audience | Prerequisites | Content details | Scheduled events
About the course
Expression proteomics aims to characterise the diversity and abundance of proteins within a biological system. This workshop provides a practical introduction to the bioinformatic analysis of expression proteomics data using dedicated Bioconductor packages in R.
Participants will work with a real-world dataset generated from a tandem mass tag (TMT) mass spectrometry experiment. The course covers the core data structures used to store and manipulate protein abundance data, approaches for quality control and filtering, and methods for visualising and exploring proteomics datasets. Participants will also learn how to perform differential abundance analysis between sample groups and how to interpret results through gene ontology analysis.
By the end of the workshop, participants should be able to analyse expression proteomics datasets from data import through to biological interpretation.
Teaching is primarily hands-on, with short presentations introducing the concepts and methods needed to analyse expression proteomics data.
Back to topIntended audience
This course is suitable for:
- proteomics practitioners who want to analyse expression proteomics data in R
- data analysts and bioinformaticians interested in proteomics data analysis workflows
- researchers seeking practical experience with Bioconductor packages for proteomics analysis
- participants interested in analysing and interpreting quantitative mass spectrometry data
Familiarity with mass spectrometry or proteomics is desirable but not essential, as the course includes an introduction to typical mass spectrometry experiments and data.
Prerequisites
Participants should have:
- a basic understanding of mass spectrometry
- a working knowledge of R and the tidyverse
For an overview of mass spectrometry technologies, we recommend watching this iBiology video.
The following experience is useful but not required:
- familiarity with Bioconductor data classes, including those commonly used for RNA-seq analysis
Content details
The course covers the following topics:
- Introduction to expression proteomics
Introduces the principles of expression proteomics and explains how mass spectrometry can be used to quantify protein abundance. - Peptide quantitation and quantitative proteomics methods
Covers approaches for peptide quantitation and introduces common strategies used to measure protein abundance in mass spectrometry experiments, including tandem mass tag (TMT) experiments. - Importing proteomics data into R
Introduces methods for importing data into R and Bioconductor from software packages such as Proteome Discoverer, MaxQuant, and FragPipe. - Data structures for proteomics analysis
Covers the dedicated data structures used to store and manipulate multi-dimensional protein expression datasets in Bioconductor. - Quality control and filtering
Introduces approaches for assessing data quality, filtering low-quality observations, handling missing values, and summarising peptide-level measurements to protein-level abundance estimates. - Data visualisation and exploratory analysis
Covers methods for visualising proteomics datasets to assess data quality and explore patterns in protein abundance. - Data normalisation
Introduces the importance of data normalisation and discusses methods for correcting technical variation in quantitative proteomics experiments. - Differential abundance analysis
Covers statistical approaches for identifying differentially abundant proteins between groups of samples and interpreting the results. - Functional interpretation of proteomics results
Introduces approaches for annotating differential abundance results using gene ontology analysis and relating protein-level changes to biological processes and functions.