Analysis of single cell RNA-seq data PrerequisitesNew
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 available for the analysis of scRNA-seq.
Materials for this course can be found here.
Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book by linking here.
- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
- Further details regarding eligibility criteria are available here
- Further details regarding the charging policy are available here
- 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 recommend attending the Introduction to RNA-seq and ChIP-seq data analysis or the Analysis of high-throughput sequencing data with Bioconductor before attending this course.
Number of sessions: 1
| # | Date | Time | Venue | Trainers | |
|---|---|---|---|---|---|
| 1 | Wed 24 Feb 2016 09:30 - 17:00 | 09:30 - 17:00 | Bioinformatics Training Room, Craik-Marshall Building | map | Vladimir Kiselev, Martin Hemberg, Tallulah Andrews |
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
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
Presentation and demonstrations
1
A number of times per year
- Introduction to RNA-seq and ChIP-seq data analysis
- Analysis of high-throughput sequencing data with Bioconductor
Booking / availability