This course provides a practical introduction to single-cell RNA-sequencing analysis, helping you understand how single-cell transcriptomics can be used to study cellular heterogeneity and gene expression at individual cell level.
Using published datasets, you will learn how to move from sequencing outputs to downstream analysis, exploring how single-cell methods can support biomedical research questions.
Through hands-on learning in R, including the use of the Seurat package, you will process and analyse single-cell gene expression data, perform quality control, normalisation, clustering, differential expression and trajectory analysis, and visualise your results.
By the end of the course, you will understand the main applications of single-cell sequencing, the advantages and limitations of different methods, and how to apply a standard single-cell analysis workflow to real datasets.
