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.
Organisation
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:
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.
At the end of the course, the participants are expected to:
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.
Universität Bern, room 304 / 3 floor East, Hochschulstrasse 4.
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 |
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. |
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 |
We will recommend 0.5 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.
For more information, please contact training@sib.swiss.