Gene expression data analysis using R: How to make sense out of your RNA-Seq/microarray data

Course date: 22 June 2015 - 26 June 2015
Location: Erasmus MC, OWC-22

Click here for details of programme

Prior to this course we're also organizing a basic course in R (click here for the link).

Introduction & target audience
Five days hands-on computer course for biological and clinical researchers whose research involves experiments that generate gene expression data. The course focuses on microarray and next-generation sequencing gene expression data, but some concepts may be applicable to other types of genomics data. Most of the speakers (and therefore examples) have a biomedical background. Software packages used are freeware, including the statistical software R, Bioconductor, Cytoscape and web tools. The total number of participants is limited to 40.

Learning objectives
1. The participant has insight in the issues involved in good experimental design, including power to detect differential expression in microarray and next-generation sequencing data.
2. The participant knows and can perform analysis steps in expression data analysis, visually present and judge the results for:
- quality control and preprocessing,
- finding differentially expressed genes,
- cluster analysis,
- classification analysis,
- pathway testing.
3. The participant has insight in the different algorithms and options available to perform an analysis, and can make an informed choice.
4. The participant knows the pitfalls of existing analyses and is able to critically judge the statistical analysis of expression data performed by others.

Pre-requisites for participants
Participants need to know what a microarray experiment is, and have their own expression profiling data. They have preferably followed an introduction to R course; alternatively they have practiced the "Getting started in R" practical prior to the course. Basic statistical concepts including mean, variance, standard deviation, probability distributions, t-test, p-value, correlation, and linear regression are assumed known. These are typically seen during basic statistics courses.

Please fill in the online registration form (in the free text box at the bottom of the form):
• do you have basis R knowledge (yes/no); if yes, please indicate how you acquired this knowledge: basic R course/ other…;
• do you have gene expression data to analyse yes/no, if yes: which platform? Affymetrix/ Illumina microarrays/ Agilent/ other: .....

Registration, deadline and admittance
The total number of participants is limited to 40. There are still places available.

The course is intensive, and covers the basic concepts and methods required for expression data analysis. Presentations are followed by hands-on computer sessions to directly apply and get more insight in the analysis methods. One afternoon is dedicated to the analysis of a new data set, allowing the students to refresh and extend their analysis skill. After the course, the presentations, practicals and test data will remain available for future reference. Software packages used are freeware, including the statistical software R, Bioconductor, Cytoscape and web tools.

Speakers and moderators
• Judith Boer and Alex Hoogkamer, Department of Pediatric Oncology, Erasmus MC-Sophia Children's Hospital, Rotterdam
• Henk Buermans, Leiden Genome Technology Center, LUMC
• Marcel Reinders and Erdogan Taskesen, Information and Communication Theory Group, TU Delft
• Renée de Menezes, Department of Epidemiology and Biostatistics, VUmc, Amsterdam
• Lodewyk Wessels and Jelle ten Hoeve, Bioinformatics and Statistics Group, Netherlands Cancer Institute, Amsterdam
• Jelle Goeman, Department of Biostatistics, Radboud University Nijmegen
• Maarten van Iterson, Department of Molecular Epidemiology, LUMC Leiden
• Guido Jenster, Department of Urology, Erasmus MC
• Andrew Stubbs, Department of Bioinformatics, Erasmus MC, Rotterdam
• Kristina Hettne and Eelke van der Horst, Biosemantics Group, LUMC Leiden
• Guido Hooiveld and Peter van Baarlen, Nutrition, Metabolism & Genomics Group, Wageningen University
• Course website: Sylvia de Does, Department of Bioinformatics, Erasmus MC

Course material from previous courses

Here you can find the website with course material, also from previous courses:

Full participation in this course is 2,0 ECTS

Course organizers
Program: Dr. Judith Boer
Pediatric Oncology, Erasmus MC-Sophia Children’s Hospital, and Human Genetics, Leiden University Medical Center,
Coordination: Dr. Frank van Vliet, Managing Director MolMed, 010-70 43518/ 06-5474 6408,

Related courses
LUMC will organize a basic course in R from 1 until 4 July; see:; preference for registrations from LUMC. MolMed (Erasmus MC) had organized several courses on R and the next one is open for registration without date, but will be scheduled soon.

Attendance fees
Course tuition for non-commercial participants is € 700. Discounts are handled as followed:

  • Participants from the postgraduate school MolMed receive a discount of 100%.
  • PhD students and Master’s students, regardless of institution, get a discount of 50% (tuition = €350).

General information
Financial regulation of the Postgraduate School Molmed

From August 1st, 2012 the following financial rules apply:

Non-commercial course, symposium or workshop fee: €200/day, €750/week

• Participants of the postgraduate school MolMed: discount of 100%.
• Other Erasmus MC participants, e.g. of MGC, COEUR, NIHES: discount of 50%.
• PhD students and Master’s students, regardless of institution: discount of 50%.
• Master’s students who are paying the fee from their personal budget: discount of 75%.

There is one fee for the entire course. There is no reduction for partial participation.

The SPSS Course and the R Course have shorter course days. The SPSS Course counts as a 2-days course and the R Course as a 3-days course.

Commercial participants & sponsors: companies are invited to inquire about commercial participant tuition fees and about sponsoring.

Those who register for a course, symposium or workshop agree with these financial conditions.


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