Analysis of single cell RNA-seq data: 2018 BioInfoSummer Workshop
Stephanie Hicks (stephaniehicks)
The material for this work was kindly borrowed with permission and adapted from the fantastic online course Analysis of single cell RNA-seq data from Vladimir Kiselev (wikiselev), Tallulah Andrews (talandrews), Jennifer Westoby (Jenni_Westoby), Davis McCarthy (davisjmcc), Maren Büttner (marenbuettner) and Martin Hemberg (m_hemberg).
The material in the course above covers about 1.5 days and we will be taking a subset of the material for our 2-3 hour workshop for 2018 BioInfoSummer.
1.2 About the course
Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The main advantage of scRNA-seq is that the cellular resolution and the genome wide scope makes it possible to address issues that are intractable using other methods, e.g. bulk RNA-seq or single-cell RT-qPCR. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.
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 available. 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.
The orginal and complete course material is available at:
The adapted material for this course at BioInfoSummer 2018 is available at:
The course material is available on the course GitHub repository which can be cloned using
The license from the original course material is licensed under GPL-3 and that license is maintained here. Anyone is welcome to go through the material in order to learn about analysis of scRNA-seq data. If you plan to use the material for your own teaching, the original authors have requested that they would appreciate it if you tell them about it in addition to providing a suitable citation. Please contact the original lead author Vladimir Kiselev.
The course is intended for those who have basic familiarity with Unix and the R scripting language. We will also assume that you are familiar with mapping and analysing bulk RNA-seq data as well as with the commonly available computational tools.