Period (from-till): 22 November - 10 December 2021
Lecturers:
Julia Drylewicz, CTI, UCMU, 50%
Aridaman Pandit, CTI, UMCU, 50%
Terry Huisman, CTI, UMCU 100%
José Borghans, CTI, UMCU, 50%
Assisting PhD students and postdocs, CTI, UMCU, % depending on number of students
Course description:
This course will focus on different computational techniques that are currently used to study the immune system.
It will cover a range of biostatistical techniques, computational techniques to handle big data, and mathematical models.
With these concepts, you will be able to design a study, analyze and interpret the resulting data, and critically read immunological papers using computational techniques.
Each day, introductory lectures on specific topics will be followed by computer practical sessions in which participants will put into practice the introduced techniques.
Literature/Study material:
- The primary study material will consist of handouts, specifically made for this course
- Recommended reading for students without immunology background: Alberts, Molecular Biology of the Cell, Chapter 24 (Pathogens, Infection, and Innate Immunity) and Chapter 25 (The Adaptive Immune System)
Course content:
- Introduction to R (1 hour on d1)
- Brief biostatistical recap (half a day on d1)
- How to determine sample size for experiments?
- Basic statistical tests and statistical analysis
- Distributions. What is a p-value? When to correct for multiple testing?
- Linear regression
- More advanced Biostatics (1.5 days on d1-d2) [Bridge to deliver tools for next part of the course]
o Non-linear regression, GLM
o Model prediction (example on biomarkers), training and test sets
o Dimension reduction (PCA, MDS)
o Analysis of RNAseq data and big data (luminex, Olink, FACS data):
- Data analysis (Volcano plots, MA plot, how/why/when to normalize data?)
- Visualising omics data: Heatmaps, tSNE (including the paper comparing them)
o Clustering: Non-supervised versus supervised (k-means, Lasso, cross-validation, PLS, UMAP)
o Case studies, other applications of NGS data
- T-cell receptor repertoires, and/or
- HIV sequence analysis: Virus diversity
- Single-cell sequencing data and regulatory networks (hands-on)
- Basics of networks
- Gene regulation
- Design of omics experiments
- Mathematical modeling (1.5 days on d7-d8)
o Measuring cell dynamics (counter-intuition: telomeres, TRECs), including introduction into ODEs
o Host-pathogen interactions (HIV, SIR type models)
- Self-study (d8-d9)
- Exam (d10)
Registration:
Apply for this course via Osiris Student. More information about the registration procedure can be found here on the Studyguide.
Max number of students is 25.
Contact:
José Borghans (course content); j.borghans@umcutrecht.nl
I&I Secretariat (Course registration & Osiris); secretary.iimaster@umcutrecht.nl
Mandatory for students in own Master’s programme:
No
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
This course is open for students with and without a computational background. It assumes basic knowledge of I&I.
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