Advanced topics in single-cell transcriptomics

This course is now full with a waiting list.

Due to the COVID-19 situation, this course will be streamed only for the registered participants.

Overview

In recent years, single-cell transcriptomics has become a widely used technology to study heterogeneous and dynamic biological systems. A large number of new tools and approaches have been developed for analyzing this new type of data. This course aims at discussing a selection of more advanced topics in single-cell transcriptomics data analysis, such as methods that are still being actively developed and go beyond the classical and well established analysis workflows. The typical steps in single-cell transcriptomics analysis will not be covered in this course and familiarity with them is considered a prerequisite.

Audience

This course is intended for computational biologists who are already familiar with single-cell transcriptomics analysis, who wish to learn about ongoing and future developments in the field.

Learning objectives

At the end of the course attendees will:

• be able to seamlessly integrate R and python in a single workflow
• know about alternatives to CellRanger for quantifying droplet single-cell data
• know how to perform RNA velocity analysis and how to generate input counts
• become familiar with a widely used spatial transcriptomics technique
• know how to work with on disk data that may not fit into memory
• become familiar with Deep Generative Networks and their use in single cell analysis

Prerequisites

Knowledge / competencies

• Extensive hands-on experience with analysis of scRNA-seq data, corresponding to an introductory Single-cell analysis course
• Ability to use packages and modules and write analysis scripts in R and Python
• Familiarity with software containers and git version control systems is a plus

Technical

Attendees should have a computer with a reliable connection to the internet that is equipped for video conferencing (headset or microphone/speakers, webcam). A browser (firefox or chrome are recommended) and Zoom have to be pre-installed. An online R / RStudio and python environment will be provided.

Program

First day

• Combining R and python in a single analysis workflow
• Alternatives to CellRanger for quantifying droplet single-cell data

Second day

• Estimation of spliced/unspliced counts and RNA velocity analysis
• Spatial transcriptomics

Third day

• Working with on-disk data
• Deep Generative Networks for single-cell RNA-seq analysis

Application

In order to provide active support and interactions, the number of participants to this course is limited. If there are more applications than available slots, participants from different groups with a demonstrated background required for this course and prior experience in single-cell analysis will be prioritized.

The registration fees for academics are 180 CHF. Participants from non-academic institutions should contact us before application.

Deadline for registration and free-of-charge cancellation is set to 29/04/2020. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.

You will be informed by email of your registration confirmation.

Venue and Time

This course will be streamed from 9:00 to circa 17:30 and allows participants and trainers to directly interact (only for registered participants).

Additional information

Coordination: Grégoire Rossier

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.

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.