PhD student's survival guide to data analysis for becoming your own bioinformatician
- irvallebioinfo
- Mar 26
- 2 min read
Data analysis can be intimidating, but remember you are not wasting thousands of euros in samples and reagents if you mess up, just make sure not to change the raw data. Enjoy!
Before you get started programming, this details how scientific computing projects should be managed for maximum efficiency (ideally)
Learning R from the very start:
R Tutorial (Includes statistics, more detailed)
More advanced in depth explanation on visualizing, transforming and importing data with R. Relevant R specific iteration advice (I haven’t tried Quarto but it looks nice)
How to keep track of your versions, very useful for long and/or collaborative projects
Easy to lookup instructions for common data analysis tasks
Find relevant packages for different topics
Cheatsheets for useful packages like dplyr, tidyr, purrr…
This book provides a self-contained introduction to the analysis of biological data using the R programming language. Topics include principles of experimental design principles of frequentist statistics, simple statistical tests, analysis of variance, regression, analysis of categorical data, and non-parametric statistics.
Machine learning in R
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AI resources might be useful BUT DO NOT trust it blindly please
More resources:
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