Bring Your Own Data: Create figures in R using ggplot2 (2023-III)

Course description

Sharing, presenting, and publishing life sciences results requires to perform statistics and to make figures. A combination of large data sets, performing the right statistics and being able to code can make this a challenge. With this course we offer you the opportunity to Bring Your Own Data (BYOD) and learn how to use the R package ggplot2 to make high-quality figures, including assistance in choosing the correct statistics to apply to your data. All hands-on and under the supervision of experienced trainers. The objective is to have generated a manuscript-ready figure at the end of the course. In addition, you will have obtained valuable experience to generate additional figures.

Basic understanding of R and R-studio is required. If you do not have this yet, consider taking the course Introduction to R for Life Sciences first.

Note: Please bring processed data ready to be analysed. Raw sequence data is not suitable for this course, please generate count tables first. You are also very welcome to take other data types than sequencing data. If you are in doubt if the data is suitable, feel free to contact the trainer.

Important: for this course you need to bring your own laptop.

Learning objectives

• Make figures to visualise your data in order to interpret the data.
• Make high-quality, near publication ready figures, using the R package ggplot2.
• Become aware of the visualization options, such as different plot types and use of colour schemes.
• Apply the appropriate statistics to analyse your data.

Instructional method

For this course you will work on your own laptop. One week before the course starts you receive an email with instructions on how to install the software. We also ask you to inform us which dataset you would like to bring. The course will be a combination of in-class lectures, hands-on questions and answers sessions, and working 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 show your progress.

Trainer

You will be trained by one of the professionals of Utrecht Bioinformatics Center, Utrecht University.

Group size            

12 to 20 participants

Number of credits                                 

1.5 EC

Course schedule

Study load

In addition to the scheduled sessions, you should consider an extra study load of approximately 4 hours in total. This study load mostly consists of applying the learned skills on your own data to prepare questions for the next session.

Course certificate                                

You will receive a course certificate after actively participating in all course sessions, including presenting and discussing one of the figures you have made.

Cancellation and No-show policy

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.

Unfortunately we don’t offer this course for participants not part of the GSLS. Our courses tend to be fully booked by GSLS PhD candidates.