Description: 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.

Target audience:

• 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

Duration: 1

Sessions:

Number of sessions: 1

Format: Presentation and demonstrations

Frequency: A number of times per year

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 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.

Aims: 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

Objectives: 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

Related courses:

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

Theme: Specialized Training