Single Cell RNAseq Data Analysis

Description

This workshop will introduce the best practice bioinformatics methods for analysing single cell RNA-seq data via a series of lectures and computer practicals. The total course duration is 5 days which includes 4 days of lectures and exercises followed by a "Bring Your Own Data" (BYOD) day where you get to practice your skills on a dataset of your choosing. Please note that the BYOD section will take place the week after the lectures.

Practical exercises will be performed using either R or Python, so we only accept students that fullfil the entry requirements below.

Topics covered

• Overview of the current scRNAseq technologies
• Basic overview of pipelines for processing raw reads into expression values
• Quality control and normalization
• Dimensionality reduction techniques
• Data integration and batch correction
• Differential gene expression
• Clustering techniques
• Celltype prediction
• Trajectory inference analysis
• Analysis of spatial transcriptomics datasets
• Comparison of different analysis pipelines such as Seurat, Scran and Scanpy

Learning outcomes

Upon completion of this course, participants will be able to

• run analysis on your own data
• select appropriate analysis methods
• interpret the results of the analysis

Pre-requisites

The following is a list of skills required for being able to follow the course and complete the exercises:

• You are familiar with basic UNIX command line (bash)
• You are familiar with either R and/or Python
• You already understand the basics of NGS technologies
• You are familiar with analyses of bulk RNA-sequencing data
• You have full access to your computer (admin permissions). Instructions on installation will be sent by email to accepted participants.

Level

intermediate