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Analysis of single-cell RNA-seq data


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


Learning 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
  • Use available methods for pseudo-time alignment



Book Here

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