An introduction to R for neuroscientists

I plan to give a one-hour, rough and ready, introduction to R for Neuroscientists over Zoom. R is very popular programming language within some discplines (e.g. computational biology). It is however little used within Neuroscience compared to other tools such as Matlab or Python. In some ways, it should not matter what you use, and my advice is to use what you are comfortable with and what gets the job done most efficiently.

With that in mind, I will introduce R through some examples geared towards Neuroscientists. I provide a rough one hour outline below, open to change as I prepare the materials.

I'm looking for up to ten volunteers to try out the material, and then we can run it again if it is helpful (or quietly forget it if not...)

2020-05-19: update. First session went well, although we only completed first case study. I plan to give two more introductions this week to Cambridge, and then may open it up for a wider range of neuroscientists, once feedback from local participants is assessed.

Outline

Preliminaries.

Install R and Rstudio desktop. Brief introduction to why they are useful. The session will work best if you can install these two programs before the session so you can follow along. Introduction to CRAN (Comprehensive R Archive Network).

Grab the folder of materials from master.zip and unpack it to find a folder called "neuRo-master". We will be working in there.

If you are stuck, you can get to work "in-the-cloud" via the "launch binder" link at the top of this page; nothing will be installed on your computer.

Acknowledgements

Thanks to Dr Dervila Glynn and Ms Arielle Bennett-Lovell (Cambridge Neuroscience). Data for imaging experiments kindly provided by Professor Evelyne Sernagor (University of Newcastle).

Case study one: changes in firing rate in response to antagonists.

You have some recordings of neural activity in response to control and drug conditions. Did your drug cause a significant change in firing rate? Rather than rushing to perform a t-test, we will examine a modern computational approach, that is much more intuitive, to assessing the significance of firing rates.

For more details, see the firingrates folder.

Case study two: analysis of calcium imaging data.

You have some calcium imaging data reporting the simultaneous activity levels of a group of neurons, stored in a spreadsheet. We will load the data in and interrogate the data to discover properties about the neurons, writing some simple functions.

For more details, see the imaging folder.

Case study three: modelling action potentials.

(Only if there is plenty of time, probably not). We will examine how a modern simplification (the Izhikevich model) to the traditional Hodgkin-Huxley system.

For more details, see the model folder.

Next steps

Where to go next to learn more about R

I have some lecture notes (that may be recorded over coming months). Bioinformatics core facility in Downing Site often runs R courses.

Many good online resources (e.g. books) including R for Data Science.

CUP have a nice short book (although not biological enough) Braun and Murdoch. There is also a fab new book Modern statistics for Modern Biology that is available from CUP, but also free online.