The WSBIM2122 course is a project-based course that teaches the analysis of quantitative omics data. It is composed of three major parts:
Introductory material about (advanced) usage of R markdown for reports and slides, linear models, and utilisation and interpretation of gene set enrichment and over-representation analyses (GSEA).
Project 1: analysis and interpretation of RNA-Seq data, including high throughput sequencing (HTS) data processing.
Project 2: analysis and interpretation of quantitative proteomics, including mass spectrometry data (MS) and its processing.
Each project will entail an introductory lecture, dedicated office time for students to ask questions, oral presentations of the data analysis and interpretation, and submission of a fully reproducible R markdown report. The students will be able to amend their final reports after the presentations, benefiting from in-class feedback and discussion. The report will contain an introduction, a description of the experimental design of the data, a detailed description of the data analysis steps, discussion and interpretation of the results, including a biological interpretation, as well as references and contribution statement of all authors.
The projects will be done in groups, with different groups for each project. The number of students per group will be set once the total number of students is known.
Tentative schedule for the course.
The course will be taught in English. The reports and oral exams can be either in English or in French. The final mark will be based on the submitted reports and the oral exam, tentatively about 50% each.
Students taking this course should be comfortable with data analysis and visualisation in R. Formal pre-requisites for students taking the class are the WSBIM1207 and WSBIM1322.
Software requirements are documented in the Setup section below.
This material is written in R markdown (Allaire et al. 2024Allaire, JJ, Yihui Xie, Christophe Dervieux, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, et al. 2024. Rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown.) and compiled as a
book using knitr (Xie 2024bXie, Yihui. 2024b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.) bookdown (Xie 2024aXie, Yihui. 2024a. Bookdown: Authoring Books and Technical Documents with r Markdown. https://github.com/rstudio/bookdown.). The source
code is publicly available in a Github repository
https://github.com/uclouvain-cbio/WSBIM2122
and the compiled material can be read at http://bit.ly/WSBIM2122.
Contributions to this material are welcome. The best way to contribute or contact the maintainers is by means of pull requests and issues. Please familiarise yourself with the code of conduct. By participating in this project you agree to abide by its terms.
If you use this course, please cite it as
Laurent Gatto and Axelle Loriot. UCLouvain-CBIO/WSBIM2122: Omics data analysis. https://github.com/UCLouvain-CBIO/WSBIM2122.
This material is licensed under the Creative Commons Attribution-ShareAlike 4.0 License.
We will be using the R environment for statistical computing as main data science language. We will also use the RStudio interface to interact with R and write scripts and reports. Both R and RStudio are easy to install and works on all major operating systems.
Once R and RStudio are installed, a set of packages will need to be installed. See section 12.4 for details.
The rWSBIM2122 package provides some pre-formatted data used in this
course. It can be installed with
and then loaded with
Page built: 2024-11-08 using R version 4.4.1 (2024-06-14)