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Analysis of single cell RNA-seq data
22nd June 2016 @ 9:30 am - 5:00 pmFree
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.
- 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
Number of sessions: 1
|Wed 22 Jun||09:30 – 17:00||Bioinformatics Training Room, Craik-Marshall Building, Downing Site||Vladimir Kiselev , Martin Hemberg , Tallulah Andrews|
Format: Presentation and demonstrations
Frequency: A number of times per year
- 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
- Introduction to RNA-seq and ChIP-seq data analysis
- Analysis of high-throughput sequencing data with Bioconductor
Theme: Specialized Training