Analysis of single cell RNA-Seq data from 10x Genomics

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. These scRNA-seq datasets can be used to unravel heterogenous cell populations, for the discovery of new cell types and states, the reconstruction of developmental trajectories and fate decisions, all previously masked in bulk transcriptome analyses. 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methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.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 advantages and pitfalls of scRNA-Seq and go through the whole scRNA-seq analysis pipeline. We will teach you how to do proper quality control and filtering on gene level and cell level, how to do create tSNE plots, how to get potential markers for a subset of cells ... We will also look into the available trajectory inference models in order to model dynamic processes such as cell cycle or cell differentiation.

Participants should have some experience with R.  You can attend the Basic statistics in R​ training to get suffcient R background. 


4 and 5 February 2019, from 9h30 to 17h00


iGent Tower
Technologiepark Zwijnaarde 15
9052 Ghent


Liesbet Martens and Niels Van Damme

Registration start date:

19 November 2018

Registration end date:

21 January 2019