Single-cell RNA-Seq Analysis

28 - 30 November 2018
Cancellation deadline:
14 November 2018
Vincent Gardeux, Panagiotis Papasaikas, Atul Sethi, Sebastien Smallwood and other invited speakers
0.5 credits

No future instance of this course is planned yet
Please note that this course is over subscribed, and the waiting list is already too long. We are not accepting applications any more.
# Overview Over the last few years, single-cell RNA-sequencing (scRNAseq) has emerged as a revolutionary tool with the potential to explore biological heterogeneity at the most basic level of organismal organization. Several questions inaccessible in the context of bulk RNAseq can now be addressed or at least probed in a meaningful manner. Examples include the possibility for detailed mapping of the cellular composition of tissue types and identification of novel cell-types, characterization of their individual transcriptomes, discrimination of transcriptional variation arising at the single-cell versus the cell-ensemble level and highlighting the contribution of individual cells to tissue differentiation, development and disease progression.

This promise comes along with multiple technical and computational challenges. While scRNAseq data is structurally similar to bulk RNA-seq, the paucity of starting material combined with multiple confounded sources of variance result in low signal to noise ratio exemplified by high abundance of zeroes in the gene expression matrices. In this new setting, existing techniques need to be modified or novel approaches need to be developed for downstream analyses.


Course 28-29 November This course will be composed of 2 days of lectures and hands-on of single-cell transcriptomics techniques put together by members of three SIB groups (from FMI, Unibas & EPFL) working on scRNAseq data analysis. The hands-on session will consist of computer exercises that will enable the participants to familiarize with real datasets of single-cell analysis.

It will consist of:

  1. A general overview of single-cell transcriptomics, and single-cell based sequencing technologies. In particular, we’ll discuss the limitations of bulk workflows that can be overcome with single-cell analyses, as well as the advantages and limitations of single-cell analyses in gathering quantitative data.
  2. A practical session highlighting several of the most critical and common issues associated with the computational analysis of scRNAseq data:
  • Characteristics, comparison and limitations of scRNAseq data generated from the different protocols and commercially available systems.
  • Data structures, pre-processing and quality control.
  • Data visualization and detection of biologically meaningful subpopulations
  • Differential gene expression.
  • Sources of biological and technical variation and circumvention of confounding effects.
  • Pseudotime ordering and trajectory estimation.
  1. A practical session featuring ASAP, a web-based portal for the interactive analysis of scRNA-seq datasets. It will highlight the major tools used for scRNAseq analysis, as well as their limitation. Participants are invited to bring their own datasets for an interactive analysis, if interested. Details will be provided as to how the field is evolving and what are the coming challenges, with a practical example from the Human Cell Atlas initiative.

This tutorial is designed as a guided conversation through scRNAseq analyses combining lecture and hands-on sessions. It intends to give audience a feel for the data and walk them through major analyses techniques and concepts using illustrative examples and R-scripts that are applicable/extendable to most commonly available types of scRNAseq data.

Minisymposium 30 November morning

A half-day of minisymposium will follow which will consist of short talks from SIB researchers on the applications of scRNAseq technologies. The minisymposium will end with a panel discussion between speakers and the audience, letting the opportunity to debate on the advantages and pitfalls of these technologies for research projects.


This course is addressed to computational biologists, bioinformaticians, and molecular biologists involved in transcriptomic data-analysis with any level of experience and an interest in the analysis of scRNAseq data.

Learning objectives

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

  • Gain basic knowledge about scRNAseq protocols and kind of data produced by them.
  • Become familiar with basic data-structures used in scRNA-seq analysis, perform basic QC, filtering ( reads, cells, genes ), and normalization of scRNAseq data.
  • Detect possible sources of technical and biological confounding variables (e.g. library complexity, cell cycle, etc.). Apply techniques to remove or account for these confounders in subsequent analyses and evaluate their strengths and weaknesses.
  • Identify scRNAseq specific challenges in visualization and clustering for subpopulation detection, and population marker identification.
  • Perform differential gene expression analyses of genes across subpopulations, pseudotime ordering and trajectory estimation.
  • Implement aforementioned concepts with practical examples from publicly available scRNAseq datasets using custom R-scripts provided in the tutorial.
  • Evaluate the applicability of specific tools on different data types and problem settings/contexts.


Knowledge / competencies

Participants should have working knowledge of R and RNA-seq data analyses. R-scripts will be provided for the hands-on session to allow for discussion on concepts and challenges in the field.


Participants should bring a WIFI-enabled laptop. We will provide access to an R-studio server with all required libraries pre-installed.


The registration fees for academics are 120 CHF and 600 CHF for participants from companies. This includes course content material and coffee breaks.

Deadline for registration and free-of-charge cancellation is set to 14/11/2018. 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

Universität Bern, room 304 / 3 floor East, Hochschulstrasse 4.


Wednesday, November 28, 2018 - Course Day 1

09:00 - 09:30 FMI/Unibas, EPFL: Welcome + Course Overview
09:30 - 10:30 EPFL: State of the art in the field of single cell RNA-Seq
10:30 – 11:00 Coffee break
11:00-12:00 FMI: Technical overview of single-cell –omics methods
12:00 – 12:30 Tutorial (FMI/Unibas, hands-on): Setup, data structures for single cell RNA-seq data, exploratory data analysis
12:30 – 13:30 Lunch break
13:30 – 15:00 Tutorial (FMI/Unibas, hands-on): Data pre-processing. Quality metrics, gene and cell filtering, decomposing technical and biological variance
15:00 – 15:30 Coffee break
15:30 – 17:00 Tutorial (FMI/Unibas, hands-on): Dimensionality reduction, visualization and subpopulation detection in the presence of confounders

Thursday, November 29, 2018 - Course Day 2

9:00 - 10:30 Tutorial (FMI/Unibas, hands-on): Differential location, scatter and shape of gene expression distributions across subpopulations
10:30 – 11:00 Coffee break
11:00-12:30 Tutorial (FMI/Unibas, hands-on): Pseudotime ordering and trajectory estimation, overview of relevant publicly available tools and resources
12:30 – 13:30 Lunch break
13:30 – 15:00 Tutorial (EPFL, hands-on): ASAP, a web-based portal for the visualization and interactive analysis of scRNA-seq data.
15:00 – 15:30 Coffee break
15:30 – 17:00 Tutorial (EPFL, hands-on & future directions): Scalable methods showcased with datasets from the Human Cell Atlas initiative. Evolution of the field and future challenges.

Friday, November 30, 2018 - Mini symposium

09:15 - 09:40 Manfred Claassen (ETHZ, SIB) (Un-)supervised learning of cell population structure from single-cell snapshot data
09:40 – 10:05 Katharina Jahn (DBSSE, SIB) Modelling tumour evolution from single-cell sequencing data
10:05 – 10:30 Charlotte Soneson (FMI, UniZurich, SIB) Extendable benchmarks and interactive exploratory analysis of single-cell RNA-seq data
10:30 – 11:00 Coffee break
11:00 – 11:25 Panagiotis Papasaikas (FMI, SIB) Batch-correction approaches in single cell RNA-seq datasets
11:25 – 11:50 Erik van Nimwegen (UniBasel, SIB) Toward inferring gene regulatory landscapes from single-cell data
11:50 – 12:30 Panel discussion
12:30 – 13:30 Standing lunch
# Additional information Coordination: Patricia Palagi.

We will recommend 0.5 ECTS credits for this course (given a passed exam at the end of the course).

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