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.
This course is part of a larger course series in Data Analysis consisting op 19 individual modules. Find more information and enroll for this module via www.ipvw-ices.ugent.be
- Overview of the course
- 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.
- 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.
- 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.
- Dataset integration and batch correction.
- Trajectory inference: dimensionality reduction for trajectory inference; trajectory inference concepts; RNA velocity.
- Differential expression between cell types, patients, and across/between trajectories.