Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). Even though scRNA-seq makes it possible to address problems that are intractable with bulk RNA-seq data, analysing scRNA-seq is also more challenging.
In this course we will be surveying the existing problems as well as the available computational and statistical frameworks for the analysis of scRNA-seq data.
Martin Hemberg, Sanger Institute
Vladimir Kiselev, Sanger Institute
Tallulah Andrews, Sanger
Davis McCarthy, EBI
Audience and Prerequisites
- The course is intended for those who have basic familiarity with Unix and the R scripting language
- We will assume that you are familiar with mapping and analysing bulk RNA-seq data as well as with the commonly available computational tools.
- We suggest attending the or the before attending this course
- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
Syllabus, Tools and Resources
During this course you will learn about
- Normalization and correction for batch effects
- Identification of differentially expressed genes and regulatory networks
- Unsupervised hard and soft clustering of cells
After this course you should be able to:
- Normalize scRNA-seq data
- Visualize the data and apply dimensionality reduction
- Use available tools for analyzing differential expression
- Use available methods for clustering
- Use available methods for pseudo-time alignment