Analysis of single cell RNA-seq data

Prerequisites

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.

The course website providing links to the course materials can be found here.

The training room is located on the first floor and there is currently no wheelchair access to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by clicking here.

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Please be aware that these courses are only free for University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
• Further details regarding eligibility criteria 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 RNA-seq data with Bioconductor before attending this course.

Number of sessions: 2

Bioinformatics, Data handling, Data mining, Data visualisation, Functional genomics, Transcriptomics

After this course you should be able to:

• Normalise scRNA-seq data using the scater package
• Visualise the data and apply dimensionality reduction
• Use available tools for analysing differential expression
• Use available methods for clustering
• Use available methods for pseudo-time alignment

During this course you will learn about:

• Normalization and correction for batch effects
• Identification of differentially expressed genes
• Unsupervised hard and soft clustering of cells

Presentation and demonstrations

• Free for University of Cambridge students
• £ 50/day for all University of Cambridge staff, including postdocs, and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
• It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
• £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 100/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

2

A number of times per year

• Introduction to RNA-seq and ChIP-seq data analysis
• Analysis of RNA-seq data with Bioconductor