Analysis of single-cell ATAC-seq data
Analysis of single-cell ATAC-seq data
to be announced
General context
This course introduces methods for analyzing and gaining insights from scATAC-seq (Single-cell Assay for Transposase-Accessible Chromatin with sequencing) data. Unlike bulk ATAC-seq, scATAC-seq provides cell-type-specific insights into chromatin accessibility, enabling a deeper understanding of regulatory landscapes at single-cell resolution.
During the course, you will learn how the ArchR tool can be used to identify cell populations and regulatory patterns. The course will also highlight how scATAC-seq data can be integrated with single-cell RNA sequencing (scRNA-seq) to uncover gene regulatory mechanisms.
- Understand the principles and data structure of scATAC-seq experiments
- Interpret results generated by Cell Ranger ATAC
- Perform bioinformatics analysis of scATAC-seq data in R using the ArchR package
- Explore strategies for integrating scATAC-seq data with scRNA-seq to gain insights into gene regulatory networks
- Apply the acquired skills to analyse and interpret your own single-cell chromatin accessibility datasets
The course combines theoretical lectures with hands-on practice in R using ArchR. Participants will apply the learned methods to real scATAC-seq data, including their own datasets.
- Basic knowledge of R language, if you have no experience with R you can follow our R Introduction training.
- Basic understanding of NGS data analysis (can be acquired by completing the e-learning course: NGS analysis – E-Learning@VIB - login required to access the course material)
- Basic familiarity with single-cell sequencing concepts (if you want to integrate scRNA-seq data with scATAC-seq data, you should have experience with single-cell RNA-seq data analysis. This can be acquired by attending Analysis of single-cell RNA-seq data. Please contact janick.mathys@vib.be to obtain a seat for this course.
- Ability to work on the Vlaamse Supercomputer (VSC) - contact janick.mathys@vib.be if you have never worked on the VSC
This course is part of the learning path Single cell data short reads.
AI methods used
This course used unsupervised machine learning techniques embedded in tools such as ArchR and CellRanger-ATAC, including dimensionality reduction, clustering, and nearest-neighbor analysis, to identify cell populations and regulatory patterns in high-dimensional data. These methods also support tasks such as doublet detection through simulation and similarity-based cell classification.
Trainers
Vanessa Vermeirssen
Vanessa Vermeirssen is professor at Ghent University. Her lab focusses on computational and high-throughput biological methods for single cell, multi-omics analysis with a focus on understanding gene regulation and signaling towards personalized medicine.
Program
Theoretical intro technology scATAC-seq
Theoretical intro data analysis scATAC-seq in R
Arrow files and quality control
Doublet inference
Dimensionality reduction, clustering, and visualization
Cell type annotation using gene scores
Visualizing Genome Browser tracks
Calling peaks
Identifying marker peaks
Lunch & coffee breaks are provided
Theoretical intro to regulatory programs from scATAC-seq and scRNA-seq
Motif enrichment
Peak co-accessibility
Integration with scRNA-seq and peak-to-gene links
Work on your own data
Lunch & coffee breaks are provided