Single-cell RNA-seq analysis

About the course  |  Intended audience  |  Prerequisites  |  Content details

About the course

Recent technological advances have made it possible to obtain genome-wide transcriptome data from individual cells using high-throughput sequencing. This course provides a practical introduction to single-cell RNA sequencing (scRNA-seq) analysis.

Participants will gain hands-on experience with widely used software packages and methodologies for processing, analysing, and interpreting scRNA-seq data. Topics include data preprocessing, quality control, normalisation, dimensionality reduction, batch correction and data integration, cell clustering, and differential expression and abundance analysis.

By the end of the course, participants should be able to independently conduct and critically evaluate analyses of scRNA-seq experiments.

Teaching is primarily hands-on, with short presentations and demonstrations introducing the concepts and methods needed to analyse single-cell transcriptomic data.

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Intended audience

This course is suitable for:

• researchers and students interested in analysing single-cell transcriptomic data
• participants who want practical experience with scRNA-seq analysis workflows
• researchers with experience of high-throughput sequencing who wish to extend their skills to single-cell data analysis
• participants with a working knowledge of UNIX and R

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Prerequisites

Participants should have:

• a basic understanding of high-throughput sequencing technologies
• a working knowledge of the UNIX command line
• a working knowledge of R

The following experience is strongly recommended:

• analysis of bulk RNA-seq data
• running analyses on High Performance Computing (HPC) clusters

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Content details

The course covers the following topics:

• Introduction to single-cell technologies
  Introduces the range of single-cell sequencing technologies and the types of data generated by different platforms, including their strengths and limitations.
• Processing raw sequencing data
  Covers the structure of single-cell sequencing libraries and processing of raw sequencing data from the 10x Chromium platform using Cell Ranger.
• Quality control and exploratory analysis
  Introduces approaches for quality assessment and exploratory analysis of scRNA-seq data using R/Bioconductor.
• Data normalisation
  Covers methods for normalising scRNA-seq data and preparing datasets for downstream analyses.
• Feature selection and dimensionality reduction
  Introduces approaches for identifying informative features and reducing data dimensionality using methods including PCA, t-SNE, and UMAP.
• Batch correction and data integration
  Covers methods for correcting batch effects and integrating data across multiple experiments or conditions.
• Cell clustering
  Introduces approaches for identifying groups of similar cells and characterising cell populations.
• Identification of marker genes
  Covers methods for identifying marker genes that distinguish cell clusters and aid biological interpretation.
• Differential expression and abundance analysis
  Introduces methods for comparing gene expression and cell abundances between conditions and interpreting the results.

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