Biological research has evolved into a data-intensive profession requiring computational and statistical abilities for meaningful data interpretation. Developing computational and statistical abilities benefits a life scientist not just in day-to-day data handling and analysis but also in career development.

As a bioinformatics core facility at the Cancer Research UK Cambridge Institute (CRUK CI), we create statistics and genomics-related open source courses that are made available to the larger community. Some of these courses were developed and delivered in collaboration with the bioinformatics training facility at the University of Cambridge. I present an overview of accessible resources and highlight a few of the courses.

• CRUK CI training resources: https://github.com/bioinformatics-core-shared-training 
• Introduction to Statistical Analysis: This course offers a review of the fundamentals ofstatistics. The practical sessions employ Shiny web applications created using the R statistical programming language.
• Introduction to Linear Modelling with R: This resource covers ANOVA, simple and multiple regression, generalised linear models, and more advanced topics such as nonlinear models and time series.
• Introduction to R for Biologists: This course provides a comprehensive introduction to the R programming language in seven bite-sized sessions. This course is built on the popular tidyverse package, making data processing and plotting more engaging.
• Introduction to bulk RNA-seq data analysis: From this resource, you can learn how to analyse RNA-seq data. This will include read alignment, quality control, quantification against a reference, reading count data into R, performing differential expression analysis, and gene set testing, with a particular emphasis on the DESeq2 analysis workflow.
• Introduction to single-cell RNA-seq data analysis: From this resource, you can learn how to analyse the droplet-based 10x genomics data, which will include running the accompanying cellranger pipeline to align reads to a genome reference and count the number of reads per gene, reading the count data into R, quality control, normalisation, data set integration, clustering and identification of cluster marker genes, and differential expression and abundance analyses.

You are welcome to use our resources, and we would appreciate it if you could acknowledge us.

Top image by the Cancer Research UK Cambridge Institute.