The one-week intensive course Statistical Data Analysis for Genome-Scale Biology teaches statistical and computational data analysis of multi-omics studies in biology and biomedicine. It comprises lectures covering underlying theory and state of the art, and practical hands-on exercises based on the R / Bioconductor environment. At the end of the course, you should be able to run analysis workflows on your own (multi-)omic data, adapt and combine different tools, and make informed and scientifically sound choices about analysis strategies.
The course is intended for researchers who have basic familiarity with the experimental technologies and their applications in biology, and who are interested in making the step from a user of bioinformatics software towards adapting or developing their own analysis workflows. The four practical sessions of the course will require you to follow and modify scripts in the computer language R. To freshen up your R skills, see below for some links.
Introduction to R and Bioconductor
The elements of statistics: hypothesis testing, multiple testing, bootstrap and permutation testing, clustering and classification, machine learning, visualisation
RNA-Seq data analysis – bulk and single cell
Computing with sequences and genomic intervals
Working with annotation – genes, genomic features, variants, transcripts and proteins
Gene set enrichment analysis
MS-based proteomics and metabolomics
Basics of microbiome analysis
Experimental design, batch effects and confounding
Reproducible research and workflow authoring with R markdown
Package development, version control and developer tools
Working with large data: performance parallelisation and cloud computing
The course consists of
Morning lectures: 20 x 45 minutes: Monday to Friday 8:30-12:00h
Practical computer tutorials in the afternoons: Mon, Tue, Thu, Fri 13:30-16:30h
Evening sessions: Mon, Tue 20:30-22:00h
A hike in the mountains and networking opportunity: Wed 14:30-22:00h
Please follow the instructions below before arriving at the course.
Due to the limited Internet connectivity at the course venue, it is necessary that you install all required software on your laptop before arriving. Because of the range of topics covered in the course you will need to download a large number of packages, so it is really helpful to do this in advance. To do so, please follow these steps.
Please install the current release version of R (version 3.6.1). For more instructions, please check https://cran.r-project.org/
Please install and set up Git. Jenny Bryan has some excellent instructions available at http://happygitwithr.com/install-git.html
If you are using Windows we recommend Git-For-Windows (https://git-for-windows.github.io/) and if you are using a Mac our suggestion is Git-SCM (https://git-scm.com/downloads)
Install a recent version of RStudio (version 1.2.1335 or higher). Instructions are available here: https://www.rstudio.com/products/rstudio/download/
(ONLY FOR LINUX USERS!) If you use Linux it is likely you will need to install some additional system libraries. An example installation of the required libraries for Ubuntu 19.04 can be found at https://www.huber.embl.de/users/msmith/csama2019/linux_libraries.html You may need to adjust this for your own Linux distribution.
After installing all of the above, please open RStudio and copy the line below into your R prompt:
The script will check if you have the versions of R, Bioconductor and RStudio that are required for the course. The script will also install the R/Bioconductor packages needed for the course. The script might trigger a question about whether you would like to update old packages, please select the option to update “all”. It will also download several datasets used during the course, and may prompt you to create new folders. It is safe to use the default options here. This process may take a long time, but it is generally OK to leave it running after the installation has started.
If you encounter any errors, please pay close attention to the messages displayed, they may contain further instructions.
For questions regarding software installation, please contact Mike Smith (mike.smith [at] embl.de).
You will work on the labs at your own pace in small groups with expert guidance (all lecturers from the morning sessions plus teaching assistants).
Participants are required to bring their own laptop with the most recent release versions of R and Bioconductor installed: R-3.6.x and Bioconductor 3.9 installed (details will be provided). Please make sure that your computer’s hardware is sufficiently powered (>=8 GB RAM, >20 GB free disk space) and that you have administrator rights. The course material will be provided by a local network wireless network – however, this not connected to the internet. Please set up your computer beforehand; internet connections at the course venue can be slow and unreliable.
One of the afternoons is for a joint cultural and outdoors activity. We plan a trip into the mountains with a (light) walk in the high-alpine area, weather permitting, and delicious local dinner.
14.50h We meet at “Casa della Gioventù” and walk 1 min to the bus waiting for us in Via Dante.
Please take the underpass to cross “Via Peter Mayr.”
15:00h The bus leaves towards the cable car for Mount Plose.
15:30h The cable car takes us to the top of Mount Plose in about 20-30 min.
45-60 min Hiking to RossAlm at 2200 m altitude.
18:00h Social dinner at RossAlm.
20:30h Time to leave! We walk back to the bus from RossAlm.
21:00h The bus takes us back to Brixen.
Solid footwear (ideal: hiking shoes or boots)
Jacket or warm sweater for the descent (it may get quite fresh)
If you don’t attend the social programme, please let us know at the latest on Monday 22 July via email to bell [at] embl.de
No Internet Access
Please note: The course venue does not provide stable internet access. The course provides a local network with the course material.
Presentations from group work on single-cell technologies
Creating and maintaining an R package (DR)
Interactive visualisation of SummarizedExperiments: iSEE (CS)
Large data, performance, and parallelization; large-scale efficient computation with genomic intervals (VJC, MM)
Machine learning (supervised) (WH) (optional)
Each talk 5 min + 2 min questions
Lecturers and Teaching Assistants
Simon Anders (SA), Simone Bell (SB), Vincent J. Carey (VJC), Laurent Gatto (LG), Robert Gentleman (RG), Wolfgang Huber (WH), Katharina Imkeller (KI), Martin Morgan (MM), Davide Risso (DR), Mike Smith (MS), Johannes Rainer (JR), Lori Shepherd (LS), Charlotte Soneson (CS).
We will welcome you on Sunday around 18:00h for a welcome buffet at the main site (1).
The main idea of this pre-course meeting is to meet each other in an informal environment, but you can also consider this as a course registration where we will give you all the course materials including the receipt of your payment.
Please note: there will be food and drinks to satisfy your hunger and thirst, and usually we move on into the town in smaller groups to enjoy the many beergardens and restaurants.
The following map is a very reduced one. You can have a big one in PNG format, or you can browse an interactive one from the web page of the Tourist Office in Bressanone-Brixen.
(1) Casa della Gioventù – main course site:
Casa della Gioventù is the place where both morning lectures and laboratory will take place, and it is also the place of the Welcome Buffet.
It is a building of the University of Padova, located in Via Rio Bianco 6, 5 minutes from the town center.
(2) Hotel Albero Verde (Grüner Baum):
It is a hotel were many of the participants usually stay.
(3) The railway station:
Where many of you will arrive.
Travel & Accommodation
How to get to Bressanone – Brixen
Bressanone (or Brixen) is in the bilingual province of Bolzano (Bozen) at 500 meters of altitude, in the Italian Alps, close to the Dolomites.
Bressanone is on the international line between Munich (Germany) and Verona (Italy). Every train on this line stops in Bressanone. Moreover, there are many commuter connections from Innsbruck (Austria) and from Bolzano (Italy) to Bressanone.
The train from Verona P.ta Nuova to Bressanone takes about 2 hours, with frequent connections throughout the day. The train from Munich takes about 3.5 hours, with connections every 2 hours.
Several airports have good train connections to Bressanone:
Bolzano, Italy (BZO) 50 km (30 minutes by train)
Innsbruck, Austria (INN) 80 km (1 hours by train)
Verona, Italy (Valerio Catullo, VRN) 200 km
Munich, Germany (Franz-Josef Strauss, MUC) 260 km
Venezia, Italy (Marco Polo, VCE) 320 km
Milano-Linate, Italy (LIN) 330 km
Milano-Malpensa, Italy (MXP) 350 km
The closest large airport is Verona (Valerio Catullo) international airport. From Verona airport to the railway station Verona P.ta Nuova (see above) there are shuttle busses every 20 minutes from 6:30am to 11:35pm. From Verona to Bressanone it takes about 2 hours by train, and you have several trains per day (see above).
About Munich: please do not confuse Munich’s main airport (Franz-Josef Strauss) with Ryanair’s “Munich West” (Memmingen) – it is far out in the Allgäu, and it is not much fun coming from there by public transport; with a rental car, it may however be an option.
Please make your reservations yourself and ask for further information by sending an email to the following address: firstname.lastname@example.org or check their web site for the telephone number.
There also exist two youth hostel-like accommodations. These locations have low fares, and they can be considered as equivalent both for quality and location (Brixen is a small town). Please ask directly them if you need rooms (with or without bath, etc).