Do you have no idea if coding is right for you regarding your analysis plans?
Do you want to learn how to analyze and conduct statistical analyses on your data quickly and efficiently?
Do you want to create beautiful publication-quality plots?
Do you have no idea where to start?
Assuming that your answer to any of these questions is "yes": You're in the right place!
The purpose of this repository is to translate some of what I've learned in the last year to other students who may be feeling just as lost as I was; in September 2020, I came into MEDG with wet lab and clinical experience, but absolutely no coding or computational skills (but wanting to learn!) in a dry lab thesis. I hope that you, the reader, can benefit from this collection of resources as a starting point to learn the "ABC"s of coding vocabulary and to distinguish which coding languages and environments are right for you; I made this with the question "What would September Me have found helpful?" in mind.
Of course, every trainee has a different background, thesis project and supervisory expectations of their analytical output - but at the end of the day, we're all generating data, and we all have a responsibility to conduct and present out analyses with methods that are as transparent and reproducible as possible, no matter what kind of research (model organism, cellular, computational, etc) that we are conducting. I hope this is helpful in contextualizing the importance of computational skills as a life sciences/genetics researcher.
1. Intro to computational analysis: What does it all mean? Click here
I wrote this "chapter 1" with the intention to contextualize commonly used terms and jargon that are frequently used in the pages below.
2. Which language is for me?
Do some Googling of what kind of work you're doing and what others say; R? Python? C++? Matlab? Look at papers to find out what your field uses to conduct their analyses. I - personally, with my novice computational skills at the time writing this - am learning/applying Bash shell script (on the command line) and R (in RStudio) for my thesis work. I'm working with large 'omics data and wrangling associated metadata. Among my colleagues, I usually hear that R is preferred as the "first" language, however eventually moving to Bash script/familiarity with the command line (and python) may be required for you depending on your analyses!
3. UBC student groups: Click here
UBC and its affiliated hospitals have many student groups who have designed and executed by students, for students. This page highlights key intro/intermediate tutorials and walk-throughs that should help you get your feet on the ground!
4. UBC resources: Click here
UBC itself has several departments with potentially relevant resources and tools.
5. Helpful non-UBC resources:Click here
The internet is wonderful but also massive; in this page are a number of pages that I - or my friends/colleagues - have referred to and recommend for trainees new to computational analysis.