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

Course date: 20 September 2021 - 24 September 2021
Location: Online Teams

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


Five days hands-on computer 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. 


Knowlegde of R required

For those of you who have no knowledge of R, it is required to work your way through the attached introduction in R. It is necessary to do so, because in this course we will not cover the basics of R but assume that you already have this basic knowledge.

Under abstracts/reviews in preparation of the course you will find a .zip file that contains: 
- instructions on how to install R
- exercises for R

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.

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. 

Course material from previous courses 

Here you can find the website with course material about R:

Course fees:
If not otherwise indicated, Erasmus MC PhD courses are free for Erasmus MC PhD candidates and Research Master students who use them as electives. Erasmus MC employees pay €100/day. External PhD candidates pay €100/day. Commercial and other external participants pay €200/day. There is one fee for the entire course, there is no reduction for partial participation.

Cancellation policy:
Erasmus MC Graduate School charges a cancellation fee of €75 for all cancellations made more than 22 days before the start of the course. Fifty percent of the course fee is due for cancellations made between 21 and 10 days before the start date. The full course fee is due for cancellations made less than 10 days before the start date, or in case of no-show without cancellation. For Erasmus MC PhD candidates the full course fee in case of the cancellation policy will be the equivalent of the number of course days times €100. 

All invoices will be sent to the head of department or for external participants to the e-mail address provided upon registration. 

Those who register for a PhD course agree with these financial conditions.


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