Analysis of single cell RNA-seq data (ONLINE LIVE TRAINING)

PrerequisitesUpdated

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.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking 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 registered 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

• Highly Recommended: Basic familiarity with the Unix command line and the R scripting language. Please make sure to attend one of our courses on these topics (see related courses below). Otherwise, please work through these materials before attending this workshop: Basic Command Line and Introduction to R for biologists
• Recommended: It will also be beneficial if you have some familiarity with the Analysis of bulk RNA-seq data. You can attend our course on this topic either before or after attending this course (link below).

Number of sessions: 3

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

After this course you should be able to:

• Know about different single-cell sequencing technologies available nowadays, their pros and cons and which you may want to use for your experiments
• Process raw single-cell sequencing data and assess the quality of your data
• Normalise scRNA-seq data
• Visualise the data and apply dimensionality reduction
• Apply methods for batch correction and data integration
• Identify groups of similar cells by clustering and identify marker genes to differentiate them
• Apply differential expression between conditions

During this course you will learn about:

• Different scRNA-seq technologies and what kind of data you obtain from each
• Processing raw sequencing data from the commonly-used 10x Chromium platform using cellranger and the Loupe browser for exploratory analysis of the data. Preparing reference genomes for mapping with cellranger.
• Use several R/Bioconductor packages for downstream analysis of scRNA-seq data, including: data normalization, correction for batch effects, dimensionality reduction methods (PCA, t-SNE and UMAP), cell clustering and differential expression analysis.

Presentation and demonstrations

• Free for registered University of Cambridge students
• £ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) 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

3

A number of times per year

• Introduction to the Unix command line (IN-PERSON)
• Introduction to R for Biologists (ONLINE LIVE TRAINING)
• Bulk RNA-seq analysis (IN-PERSON)
• Working on HPC clusters (IN-PERSON)
• Extracting biological information from gene lists (ONLINE LIVE TRAINING)