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After successfully completing this course, you:

• Understand the characteristics of RNA-seq data in general and your specific dataset in particular.
• Have an overview and understanding of the main steps in any RNA-seq analysis.
• Can design a simple workflow for processing data of various RNA-seq experimental setups.
• Base your analysis decisions on limitations, weaknesses and strong points of various types of analyses.
• Can execute and document these analyses in R(markdown).
• Can make important visualisations to describe your data, support analysis decisions and report statistical outcomes.

In this course, we will make many figures of RNA-seq data. The focus of these figures is a deeper understanding of the analysis and the data, not publication quality. We teach a separate BYOD course for this purpose: BYOD ggplot2. This ggplot2 course is a nice addition to elevate your figures to publication quality, for RNA-seq-related figures, but also for any other data type.

During this course, you will work on your own laptop. A week before the course starts, you will receive an email with instructions on how to install the required software. Classes during the week start with in-class lectures, hands-on exercises and Q&A sessions. As the week progresses, fewer exercises are provided and you will work predominantly on your own data. At the end of the course, you will shortly present and discuss one of the figures you have made during the course to share your progress.

Requirements

When you register for this course, we expect you to be proficient in basic R. See if the following statements apply to test if this is the case for you:

• You are comfortable loading, exploring and manipulating data in R.
• You need little effort to work with various data types (numeric, character, logical, factor) and various object types (vector, matrix, data frame).
• Making subsets, reordering data, and making simple plots is intuitive.
• You can write an R script that logically processes data to some end result, such as a figure or summarising table.
• With some effort, you can write simple functions and use apply and for-loops to process your data.
• You understand basic statistics such as t-tests, ANOVAs and lineair models (knowledge included in the Introductory to Biostatistics course.
• You can apply skills on the Base R cheatsheet without help: 

Experience in the following topics will help you, but is not required:

• Experience with ggplot2 for making publication-quality figures (knowledge included in the BYOD ggplot2 course).
• Experience fitting linear models to data.
• Rmarkdown and Rstudio: if you do not meet this level of R programming yet, be welcome in the Intro to R course we offer via the PhD Course Centre. Of you do neet meet the level of statistics yet, be welcome to follow the Introductory to Biostatistics course.

During this course you will work on your own laptop with R and Rstudio installed!

Study load

Next to the in-class lectures, hands-on exercises and Q&A sessions, you will spend the vast majority of your time on analysing your RNA-seq data during the week of the course. In this way you gain as much as possible from the in-class sessions.

Group size            

Groups are small to ensure sufficient supervision time for all data-set-specific questions. We allow 10 participants in the course. Depending on demand, the course may be organised more frequently.

Number of credits                                 

1.5 EC

Trainer

You will be trained by one of the professionals of , Utrecht ľ�ϸ���Ӱ��.

Course certificate                                

You will receive a course certificate after actively participating in all course sessions and passing the Final Assignment.

This course is free for GSLS PhD candidates. However: free of charge does not mean free of responsibility. Once you have signed up for a course, we expect you to attend. For every late cancellation or no-show we have had to disappoint others who would have liked to attend. This is our policy:

• You may cancel free of charge up to 4 weeks before the start of the course. After this date you can only cancel if you have a GSLS PhD candidate to replace you in the course. Send the name and contact information of your replacement to pcc@uu.nl, at least 2 working days before the start of the course;
• We expect that you actively attend the full course, but at least 80%. It is mandatory to attend the first session. If you are absent the first session you cannot follow the remaining of the course;
• Not meeting the above requirements means you will be charged a no-show fee (€ 75). We will send the invoice after the course has ended. We are unable to make any exceptions, unless you have a valid reason (i.e., illness or death in the family 1^st/2^nd degree or partner). Your supervisor has to send an e-mail to pcc@uu.nl indicating the reason.