Single-Cell Transcriptomics with R
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Overview
In contrast to the Bulk RNA sequencing used to quantify the abundance of gene and transcript expression at a whole population level, single-cell RNA sequencing (scRNAseq) allows researchers to study gene expression profile at a single cell resolution while enabling the discovery of tissue specific subpopulations and markers. For example, contrasting different sample conditions i.e. disease vs. normal using scRNAseq can help identify sub-cellular differential behaviours and thus target specific gene markers. This 3-day course will cover the main technologies as well main aspects to consider while designing a scRNAseq experiment including a hands-on practical data analysis session applied to droplet-based methods.
Audience
This course is intended for life scientists and bioinformaticians familiar with "Next Generation Sequencing" who want to acquire the necessary skills to analyse scRNA-seq gene expression data.
Learning objectives
At the end of the course, participants will be able to:
- distinguish advantages and pitfalls of scRNAseq
- design their own scRNA-seq experiment
- apply a downstream analysis using R
Knowledge / competencies prerequired (Mandatory)
Participants should already have a basic knowledge in Next Generation Sequencing (NGS) techniques, or have already followed the "NGS - Quality control, Alignment, Visualisation". Knowledge in RNA sequencing is a plus. A basic knowledge of the R statistical software is required. Test your R skills with the quiz here, before registering.
Technical requirements
Attendees should have a Wi-Fi enabled laptop. An online R and RStudio environment will be provided, but attendees who wish to run the practicals on their own laptop should install R and RStudio, as well as install the R packages Seurat, scran, scater, SingleR and monocle, before the start of the course.
Program
First day
Introduction to scRNAseq:
* Technologies
* Experimental design
* R versus GUI-based tools
Quality control
* Dropouts - Doublets
* Doublet removal using simulation
* Ribosomal / mitochondrial RNAs
* Cell cycling
Normalization and scalability
* Feature selection
* Log scaling
* Confounding factors removal
Second day
Dimensionality reduction and cell type clustering
* PCA
* tSNE
* UMAP
* Clustering methods (Hierarchical, K-means and Graph-based)
* Data integration of complex experimental designs
Cell type identification and marker identification
* Methods and applications
Differential expression analysis
* Methods overview
Third day
Differential expression analysis - continued
* DE between clusters
* DE between samples (involving data integration)
* Gene set enrichment analysis
Pseudotime analysis
* Methods and applications
Application
The course is now full. Applications will be placed on the waiting list. Should a spot become available, priority will be given to those on the waiting list.
The registration fees for academics are 180 CHF and 900 CHF for for-profit companies.
You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.
Applications will close once the places will be filled. Deadline for registration and free-of-charge cancellation is set to 01/06/2021. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.
Venue and Time
This course will be streamed.
It will start at 9:00 and end around 17:00.
Precise information will be provided to the participants in due time.
Additional information
Coordination: Patricia Palagi
We will recommend 0.75 ECTS credits for this course (given a passed exam at the end of the course).
You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.
Please note that participation in SIB courses is subject to our general conditions.
SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.
For more information, please contact training@sib.swiss.
Keywords: genes and genomes, biochemistry, biomarkers, experimental biology, functional genomics, next generation sequencing, population genomics, single-cell biology, training, transcriptomics, mauro delorenzi & frédéric schütz group
Activity log