Teaching "R for cell biologists", not teaching R to cell biologists!

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Materials for teaching "R for cell biologists": a 90 minute workshop to introduce R in a way that cell biologists would actually use it. For more information read this.

This repo is intended for instructors or for self-directed students to generate materials for the workshop.

1. Download the repo.
2. Start a new project in RStudio: File > New Project... then New Directory, name the project (suggestion: RTraining) and save somewhere on your computer
3. Open the script 00_r_project_setup.R and run it. This will make a standardised directory structure in your project folder.
4. Next, open 01_simulate_data.R and run that. This will generate a series of data files to use in the workshop. They are saved in Output/Data/

These steps will help you to familiarise yourself with the directory structure used in the workshop. You can, skip straight from step 1 to using the zip file of example data, Data.zip

Assuming all participants bring their own laptops, let them know that they meed to download and install R and RStudio ahead of the workshop. Any installation of packages can be done during the session.

The files you generated above need to be made available to the participants. Suggestion: make a compressed archive (zip) and send to the students prior to or during the workshop. They will also need the scripts 00_r_project_setup.R and 02_training.R, so distribute these at the same time.

Three concepts need to be introduced:

Emphasise reproducibility, automation, and publication-quality graphics.

A typical experiment involves setting up cells, imaging them on the microscope, analysing the images in Fiji, and the output is plain text files, one for each image. To make a figure, we need to process all of these text files and turn them into publication-quality figures using R.

We need to:

1. Read in the data
2. Do some calculations or processing (optional)
3. Make some plots

See here for more details.

Depending on the experience of the group, other concepts may need introducing: RStudio as an IDE (what the different panes are for), R as a language, scripting, 1-based vs 0-based languages.

Before we tackle steps 1-3, we have a step 0 which is to set up an R project to work reproducibly

• Start a new project in RStudio: File > New Project... then New Directory, name the project (suggestion: training_yymmdd) and save somewhere on your computer.
• Run the script 00_r_project_setup.R or paste this gist into the console and press enter.
• Using the course materials, move or copy the scripts into Script/ and the data files into Data/

Key concept: a standardised directory structure within the R Project folder helps us to easily process data and save the outputs to a standardised place. We always use the R Project folder as our working directory. It makes the project portable and doesn't rely on paths to folders on a specific computer.

Steps 1-3 execute the script 02_training.R line by line (cmd + Enter on Mac; ctrl + Enter on Windows/Linux), explaining what each line does as you go. Check for understanding throughout.

Goal: make one data frame containing all the data

• Begin by reading in one file into a data frame, explain that we have 80 files to read in.
• Show how we can read all of them in to one huge data frame using a simple command, but how do we know which rows belong to what condition and/or which experimental repeat?
• Use the filename to append information as it is read in.

Key concept: think about how you'll name the outputs of your analysis in Fiji to make reading the data into R as easy as possible.

This is an optional step. Some examples are shown but they are not needed for this exercise, as we will simply plot the data.

• Use ggplot to make some plots
• Explain grammar of graphics and demonstrate the power of facetting, theming and so on
• Explore the data, notice that one experiment is different to the others
• Make a SuperPlot
• Save the SuperPlot

Key concept: the data frame you made has all the information to make any plot you'd need.

To consolidate the learning, ask the participants to figure out how to do the following:

Which row (from which expt/cond/cell) had the lowest Mean value?

Explain that the person who did the experiments found out that the rapamycin used in the 4th experiment, was prepared from a stock solution at the wrong concentration.

How can we exclude the n4 data and remake a new SuperPlot so that all experiments used the correct concentration?

Explain that getting assistance from an LLM is unlikely to help them learn. Searching for a solution is fine. Keywords: subset/subsetting