M3 - Single Cell Seq Data Analysis Boot Camp

Type of Course - Dates - Venue - Description - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teachers - Course material - Fees - Enrol

Type of course

 This is an on campus course, with blended learning options.


November 17 and 24, December 1, 8 and 15, 2021, from 5.30 pm to 9.30 pm
Please note: the deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Registration) is October 15, 2021.


 Campus Sterre, Krijgslaan 281, 9000 Ghent, pc room 1.1 - A. Turing


The course will provide a full single-cell RNA-sequencing (scRNA-seq) data analysis pipeline, starting from raw data up to the identification of trajectories / cell types, and corresponding (marker) genes associated with the biological structure in the data. Participants can expect a mix between background theory as taught through slides and hands-on lab sessions where real scRNA-seq data will be analyzed. The course will focus on tools and methods implemented within the R / Bioconductor environment. The detailed schedule includes:

  1. Overview of the course
  2. Introduction to single-cell RNA-seq technology: concepts and protocols of bulk and single-cell RNA sequencing; RNA-seq data characteristics; research questions that can be assessed using bulk and single-cell RNA-sequencing.
  3. Preprocessing and quality control of scRNA-seq data: Processing raw FASTQ-files (demultiplexing, mapping, barcode identification); quality control (low-quality/dead cells, doublets, empty droplets); The Bioconductor infrastructure for the analysis of scRNA-seq data; Normalization of scRNA-seq data.
  4. Dimensionality reduction, clustering and cell type identification: The curse of dimensionality; linear and non-linear dimensionality reduction methods; unsupervised cell type identification through clustering; (semi-)supervised cell type identification.
  5. Dataset integration and batch correction.
  6. Trajectory inference: dimensionality reduction for trajectory inference; trajectory inference concepts; RNA velocity.
  7. Differential expression between cell types, patients, and across/between trajectories.

Target audience

This course is aimed at biologists, bioinformaticians and statisticians interested in analysing single-cell RNA-seq datasets.  


There is no exam connected to this module. Participants receive a certificate of attendance via e-mail at the end of the course.

Incorporation in DTP and reimbursement from DS for UGent PhD students

As a UGent PhD student, to be able to incorporate this 'specialist course' in your Doctoral Training Program (DTP) and get a refund of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Registration) for this course is October 15, 2021. Please note that opening a dossier does not mean that you are enrolled. You still need to enrol via the registration form on this site.

Course prerequisites

Basic knowledge of R programming and statistics is assumed as provided in Module 1 and Module 2 of this year's program.


Foto Koen Van den BergeDr. Koen Van den Berge has a background in Biology and Statistics, and obtained a PhD in statistical genomics from Ghent University. During his PhD, his work focussed on differential expression analysis of bulk- and single-cell RNA-sequencing studies, including statistical inference and multiple testing. Currently, he is a postdoctoral researcher at the University of California, Berkeley, and Ghent University, where he is developing statistical methods for normalization and interpretation of high-throughput sequencing datasets.

Foto Jeroen GilisDrs. Jeroen Gilis will teach the lab sessions. He has a background in both biochemistry and bioinformatics and is currently pursuing a PhD in data analysis at Ghent University. His main research focus is developing statistical software for differential expression analysis for single-cell RNA-sequencing datasets.


Course material

Slides and analysis scripts will be provided as an R Markdown file. Compiled results will be available on the course website.


A different price applies, depending on your main type of employment.

Employment Module 3
Industry/Private sector1 925
Non-profit, government, higher education staff2 695
(Doctoral)students, retired, unemployed2 310

1 If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second enrolment.

2 UGent staff and UGent doctoral students who pay internally via SAP or internal transfer can participate at these special prices.

Enrol for this course