Summer School Advanced Topics in Single Cell Analysis

30 August - 03 September 2021
Cancellation deadline:
28 July 2021
Alma Andersson, Åsa Björklund, Volker Bergen, Paulo Czarnewski, Emma Dann, Charlotte Soneson, Panagiotis Papasaikas, Geert van Geest
Omics data analysis
2 credits
Application are closed because the course is full with a long waiting list or has just passed. to receive notification when a new course is scheduled.

No future instance of this course is planned yet

This course is now oversubscribed with a long waiting list, and is therefore closed, thank you for your understanding.

NBIS and SIB are pleased to co-organize this special event. Part of the places will be kept in priority to members of the SIB PhD Training Network and to PhD students & researchers affiliated with SciLifeLab.


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. The goal of this joint School is to provide PhD students and postdocs with theoretical and mostly hands-on knowledge on selected advanced topics in Single Cell analysis. In particular, the participants will be split in small groups to develop mini projects. The following topics have been selected:

  • scRNAseq & Spatial Transcriptomics
  • scRNAseq & RNA Velocity and Trajectory
  • Integrative analysis of single-cell multi-omic data
    The development of single-cell assays for multiple molecular modalities other than gene expression provides a powerful tool for investigating multiple dimensions of cellular heterogeneity. Here we will be analyzing gene expression and chromatin accessibility profiles generated with scRNA-seq and scATAC-seq from the same tissue to identify genomic regions involved in cellular differentiation. We will use data integration techniques for experimental designs where both data modalities are simultaneously profiled from the same cells (vertical integration) and for designs where scRNA-seq and scATAC-seq are applied to separate groups of cells from the same tissue (diagonal integration).
  • Deep Learning for scRNAseq analysis
    Deep learning approaches have found their way on various aspects of the analysis of single cell transcriptomic data. These include deep generative models for dimensionality reduction and visualization, imputation, out-of-sample inference, batch correction, integration and exploration of data across different modalities. In this course we aim to provide an overview of the field and the different out-of-the box solutions and to take a closer look at the theoretical concepts behind those tools. In addition we will provide practical examples for the development, training and evaluation of custom architectures using ML libraries and apply them to the analysis of provided scRNA-seq datasets.


This course is addressed to participants who are already familiar with the typical steps in single-cell (transcriptomics) analysis, which will not be covered in this School.

Learning outcomes

At the end of the School, the participants are expected to:

  • have a good understanding of the most common methods of RNA-seq analysis,
  • be able to perform basic processing and evaluate the suitability of different analysis strategies for scATAC-seq data,
  • be able to integrate gene expression and chromatin accessibility data, obtained from the same set of cells or different sets of cells from the same tissue,
  • repeat the same type of analysis achieved during the mini-project,
  • present the mini-project and more globally disseminate their experience to the members of their group.


Knowledge / competencies


  • Previous intermediate experience with single cell RNA-seq analysis
  • Familiar with command line and UNIX language
  • Intermediate/advanced usage of R / Python
  • Basic knowledge of statistics

Specific to topic 4: Deep Learning for scRNAseq analysis:

  • Basic knowledge of machine learning concepts
  • Familiarity with the Keras and/or TensorFlow software libraries

A list of software to be installed in advance will be communicated on due time to registered participants.

Schedule - CET time zone

From Monday morning to Friday afternoon. Day usually start at 9h00 and end around 17h00.

Italic = Mini projects - work in small groups

Day 1

  • Introductory lecture
  • Lectures for topics 1 & 2 (see the Overview paragraph)
  • Exercise for everyone - QC + Clustering + DE detection
  • Lectures for topics 3 & 4

Day 2

  • Mini projects: Introduction
  • Mini projects: work in groups
  • Keynote lecture

Day 3

  • Mini projects: work in groups
  • Afternoon: social online activity

Day 4

  • Mini projects: work in groups
  • Two lectures: software, method
  • Mini projects: presentations preparation

Day 5

  • Mini projects: presentations preparation
  • Presentation of the 4 mini projects
  • Conclusion & wrap up


Registration is now closed.

After application, you will receive a link to complete a survey, which will allow us to analyze your background, skills and preferences about the 4 topics. This process may take some time and if you have been accepted as a participant, you will be informed by email of your registration confirmation, with a payment link.

Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 open days.

Deadline for free-of-charge cancellation is set to28/07/2021. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

Unfortunately, due to the COVID situation still in place, this School will take place online.

Additional information

Coordination: Jessica Lindvall, NBIS; Grégoire Rossier, SIB.

We will recommend 2 ECTS credits for this 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.

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