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methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.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 ...
Participants should have some experience with R. You can attend the Basic statistics in R training to get suffcient R background.
The participants will be selected based on their background and a short description of the research question. We will evaluate applications and provide feedback on acceptance in the first half of July. Please use the form below to submit the required information for the selection process. Log in before you fill in the form otherwise we do not know your name and email address !
Liesbet Martens is a bioinformatician at the VIB-UGent Center for Inflammation Research
Niels Vandamme is a post-doctoral researcher at the VIB-UGent Center for Inflammation Research.
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