Reproducible Research

As modern researchers, we should care about reproducible research.
At some point in the future, someone, somewhere, might want to repeat your analysis for themselves or re-use your data.
- which will most likely be you!
Assuming that you’ll be able to remember all the steps involved is dangerous, so making sure that everything is well-documented is key.
The documentation involves not only the methods used, but the files used as input and any transformations performed on them.

Five selfish reasons

Probably the most (in)famous example of failure to reproduce a study, which actually put people’s lives at risk and rallied statisticians into action
Keith Baggerly’s lecture on the scandal is a must-see.

R is only part of the solution
Say you use an R script to generate a list of interesting genes and come to publish
- which set of samples was used….the one with that dodgy sample removed
- didn’t you change the version of the software and re-run it?
- what filters did you apply afterwards
Need complete control over the files and data associated with your project

The key areas that we will address in this course are:-

Hopefully you will take the opportunity to learn a course to learn a language such as R or Python for your data analysis.
From experience, the biggest hurdle that newcomers to these languages face itrying to read and analyse your own data
Often not because you have not understood the course materials….
….but because the data have been managled into a form that the computer cannot process.
there’s a danger of becoming de-motivated and resorting back to whatever software you were using before.

….Not neccesarily.
Often much more convenient to eye-ball a spreadsheet and get an overall impression of your data.
But they have limitations making them not ideal for large-scale analyses.
Doing things by-hand only invites you to make copy-and-paste errors etc

Your carefully-crafted, colourful, all-singing, all-dancing, multi-tab spreadsheet probably won’t read very easily into R.
Computers are very literal don’t make any assumptions about the data. They won’t be able to tell the difference between the text entries MALE and male, Male.
Nor can they discern colours or other types of formatting in the data.
Bioinformaticians are very-skilled in the art of ‘data munging’ or ‘data wrangling’
- and all the errors in files that we will discuss have a solution in software such as R.
But would you rather they spend their time analysing the data, or manipulating it?
Check with them what format they would like the data in
- and stick to it!
- often it will be raw-data untouched by humans

At some points in the analysis process spreadsheets are unavoidable.
The metadata for the experiment is the description of what biological group each sample belongs to and other useful information such as the conditions under which each sample was derived
(e.g. batch, cage) and other characteristics of confounding factors (e.g. sex, age) that might influence the analysis.
They are often immortalised with a publication for everyone to see

Even if we take great care of our own metadata, others who make their data available or collaborate might not be so careful.
We will introduce Open Refine, a free open source, powerful tool for working with messy data that can help to transform data.

From the very beginning!
Creating documentation for our data and keeping things tidy may seem tedious
….trying to pull everything together for a paper submission is an extremely painful process.
often cited as a reason for people not sharing their data
you may have no choice about sharing your data
better to get things in shape from the start

Data formatting issues
- how can we make sure our files are readable by computers
- what mistakes to avoid
Open Refine demo
- how we can clean-up files
How to manage the files in a project
How to keep a backup
How (and why) to share with others
- and what resources are available