Authors: Christophe Vanderaa1, Stijn Vandenbulcke2, Laurent Gatto3, Lieven Clement4.
Last modified: .
Mass spectrometry (MS) has become a method of choice for exploring the proteome landscape that drives cellular functions. While technological advancements have significantly increased the sensitivity of MS instruments, obtaining reliable statistical results from these data remains a challenging and often tedious task. Many researchers continue to rely on ad-hoc analysis workflows due to a lack of clear guidelines, which can lead to violations of key statistical assumptions. In this workshop, we will offer a hands-on introduction to the msqrob2 package that provides a set of rigorously validated and benchmarked statistical workflows for MS-based proteomics. These workflows are built on the QFeatures framework for data processing. We will begin by familiarising participants with the input data format and the QFeatures data structure. From there, we will walk through the minimal data processing steps required prior to statistical modelling, explaining when and why each step is necessary. Next, we’ll explore the sources of variation inherent in proteomics data, highlighting their hierarchical structure and demonstrating how linear mixed models can properly account for these complexities. The modelling process will be carried out using msqrob2, which offers additional advantages such as robust and stabilised parameter estimation. Finally, we will demonstrate how to translate biological questions into hypothesis tests and how to prioritise proteomic markers that change in response to a condition of interest. Depending on the progress of the group, we will also briefly explore the emerging field of single-cell proteomics, discussing the additional challenges posed by these data. This workshop is designed for proteomics researchers who want to learn how to analyse their data using reproducible and statistically sound workflows, as well as for omics data analysts interested in expanding their skill set to include proteomics.
SummarizedExperiment classIf you don’t have at least two out of four prerequisites, you are still welcome to follow the workshop, but do not try to run the analysis yourself during the lecture. You should rather focus on the explanations.
Relevant background reading for the workshop:
QFeatures introduction vignette
The workshop will introduce participants to important concepts regarding the statistical analysis of MS-based proteomics and how the underlying modelling assumption relate to the experimental data characteristics. The concepts will be embedded in a real-life analysis, demonstrating the code to carry out each step from the input data up to the biological interpretation. Participants can follow the workshop by running along the analysis on their local computers, but you are not requested to as the code will be live demonstrated. We did not include exercise to allocate time for questions and interactivity with the audience.
| Activity | Time |
|---|---|
| Introduction and setup | 10m |
The QFeatures data class |
20m |
| Data preprocessing | 15m |
| Break | 15m |
| Modelling sources of variation | 20m |
| Hypothesis testing | 20m |
| Wrap up | 5m |
List “big picture” student-centered workshop goals and learning objectives. Learning goals and objectives are related, but not the same thing. These goals and objectives will help some people to decide whether to attend the conference for training purposes, so please make these as precise and accurate as possible.