svibiocellgen/mig_2019_scrnaseq-workshop

This repository contains Docker images for the SVI/MIG scRNA-seq analysis workshop (2019 version).

Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The cellular resolution and the genome-wide scope of scRNA-seq makes it possible to address issues that are intractable using other methods like bulk RNA-seq or single-cell RT-qPCR. However, scRNA-seq data poses many challenges due to the scale and complexity of scRNA-seq datasets, with novel methods often required to account for the particular characteristics of the data.

In this course we will discuss some of the questions that can be addressed using scRNA-seq as well as the available computational and statistical methods. We will cover key features of the technology platforms and fundamental principles of scRNA-seq data analysis that are transferable across technologies and analysis workflows. The number of computational tools is already vast and increasing rapidly, so we provide hands-on workflows using some of our favourite tools on carefully selected, biologically-relevant example datasets.

Across two days, attendees can expect to gain an understanding of approaches to and practical analysis experience on: quality control, data normalisation, visualisation, clustering, trajectory (pseudotime) inference, differential expression, batch correction and data integration.

Course outline:

• Day 1:

  • Morning session 1: Workshop overview; Introduction to scRNA-sequencing
  • Morning session 2: Pre-processing scRNA-seq data; quality control
  • Afternoon session 1: Normalisation and batch correction; visualisation and exploratory data analysis
  • Afternoon session 2: Latent spaces and clustering

• Day 2:

  • Morning session 1: Trajectory inference
  • Morning session 2: Differential expression; data imputation
  • Afternoon session 1: Data integration
  • Afternoon session 2: Case studies

This course has been adapted from a course taught through the University of Cambridge Bioinformatics training unit, but the material is meant for anyone interested in learning about computational analysis of scRNA-seq data and is updated roughly twice per year.

The number of computational tools is increasing rapidly and we are doing our best to keep up to date with what is available. One of the main constraints for this course is that we would like to use tools that are implemented in R and that run reasonably fast. Moreover, we will also confess to being somewhat biased towards methods that have been developed either by us or by our friends and colleagues.