
Here is a non-exaustive list of resources that our lab finds useful.
Statistics
Nature core collection: Stats for Biologists Full Collection of papers; Statistics in Biology and Practical Guides: https://www.nature.com/collections/qghhqm
Coding Nature Methods’ Points of Significance column on statistics explains many key statistical and experimental design concepts: https://www.nature.com/collections/qghhqm/pointsofsignificance
Coding general
Lists with links for online courses for machine learning and data science from MLTut.com
R
R Tutorials (from Coding Club; Uni of Edinburgh) including R Basics, Data manipulation, Data visualisation, Data synthesis, Modelling, Spatial data, Reproducible research: https://ourcodingclub.github.io/tutorials.html
R for Data Science: This website/book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it and visualize. https://r4ds.hadley.nz/
The R Graph Gallery: a collection of charts made with the R programming language:
Systematic Reviews and Meta-Analyses
PRISMA
(Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is an evidence-based minimum set of items aimed at helping scientific authors to report a wide array of systematic reviews and meta-analyses.
Most recent PRISMA statement: PRISMA 2020, 27-item checklist, an expanded checklist that details reporting recommendations: https://www.bmj.com/content/372/bmj.n71
Guides
Cochrane Handbook for Systematic Reviews of Interventions
https://training.cochrane.org/handbook/current
How to conduct a meta-analysis in eight steps: a practical guide (paper)
https://link.springer.com/article/10.1007/s11301-021-00247-4
A brief introduction of meta-analyses in clinical practice and research:
https://onlinelibrary.wiley.com/doi/full/10.1002/jgm.3312
A 24-step guide on how to design, conduct, and successfully publish a systematic review and meta-analysis in medical research:
https://link.springer.com/article/10.1007/s10654-019-00576-5
Meta-evaluation of meta-analysis: ten appraisal questions for biologists
https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0357-7
RevMan (Systematic review and meta-analysis software)
About RevMan:
RevMan Web quickstart guide
https://training.cochrane.org/online-learning/core-software/revman
RevMan Knowledge Base
https://documentation.cochrane.org/revman-kb/
RevMan 5.4 (Desktop – soon to be discontinued) User Guide:
RevMan (Desktop – soon to be discontinued) Short Tutorial Series (a playlist of 43 short videos)
metafor (R software for conducting meta-analyses)
Resources for getting started with the metafor package written by Wolfgang Viechtbauer:
Viechtbauer, W. (2010). Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36(3), 1–48. https://www.jstatsoft.org/article/view/v036i03
Metafor website: https://www.metafor-project.org/doku.php/metafor
Meta-Analysis with R Workshop: https://github.com/wviechtb/workshop_2022_ma_esmarconf
R Code for Meta-Analysis Books: https://github.com/wviechtb/meta_analysis_books
Digital Pathology
QuPath: Open source software for digital pathology image analysis (Pete Bankhead)
The original QuPath paper: https://www.nature.com/articles/s41598-017-17204-5
QuPath tutorials: https://www.youtube.com/@petebankhead/featured