Our Courses
How does it work?
The full programme, titled "Bioinformatics for Wet-Lab Biologists" is split into four logical courses, which are spread throughout the year and repeated. Course 1 is essential - "Omic Data Analysis & Visualisation Using R", covering the foundations of bioinformatics, R coding, and omic data visualisation. This course alone is sufficient for the needs of many omic projects.
Further courses are optional, dependent on need, and build on specific lessons obtained in course 1. They allow for additional specialisation into advanced topics such as single cell, spatial omics, signature analysis, clinical data, or phylogenomics. Courses do not need to be completed in any set order, except that course 1 should be completed first.
Our courses are:
Omic Data Analysis & Visualisation, Using R
Single Cell & Spatial Omics
Further Omics, Clinical Data & Statistics in R
Essential Command Line Omics & Genome Analysis
Which course do I need?
When deciding which course(s) you might need, its often best to think in terms of the type of data you have. The below table suggests courses by omic data type.
Omic Data Analysis & Visualisation, Using R
Format
Live Zoom lecture followed by a computer tutorial, using your own computer.
Either two weeks of weekday mornings 9:30am to 12:30pm.
Or 6 consecutive Mondays - 9:30am to 5:00pm, with a 1.5 hour lunch break
Dates
24th February, 2025, 6 Mondays.
2nd June, 2025, mornings for two weeks.
1st September, 2025, 6 Mondays.
8th December, 2025, mornings for two weeks.
Cost
£390 + VAT.
Summary
Essential foundations of bioinformatics, R coding, and omic data visualisation.
Theory of bioinformatics and omics. How to code in R, loading, manipulating and save omic datasets using R. How to make any type of plot using ggplot2 and make them look nice. Key omic plots and analysis techniques including PCA, expression density, volcano, MA, violin / box / jitter plots, heatmaps & clustering, pathway analysis - ORA, GSEA, URA.
Single Cell & Spatial Omics
Format
Requires Omic Data Analysis & Visualisation, Using R to be completed first.
Live Zoom lecture followed by a computer tutorial, using your own computer.
Either two weeks of weekday afternoons 2:00 pm to 5:00pm.
Or 5 consecutive Mondays - 9:30am to 5:00pm, with a 1.5 hour lunch break
Dates
2nd June, 2025, afternoons for two weeks.
20th October, 2025, 5 Mondays.
Cost
£390 + VAT.
30% discount if booked alongside another course.
Summary
Using Seurat and other R packages to explore single cell and spatial omic datasets.
List objects, the Seurat object, single cell QC, normalising and SCT transform, integration, UMAP and clustering, identifying cell / cluster biomarkers, cell type identification, plotting expression onto UMAP, differential expression between conditions, trajectory analysis, ligand receptor interactions. Visium, Xenium and CosMx spatial analysis, identifying spatial clusters, niches.
Further Omics, Clinical Data & Statistics in R
Format
Requires Omic Data Analysis & Visualisation, Using R to be completed first.
Live Zoom lecture followed by a computer tutorial, using your own computer.
5 consecutive Mondays - 9:30am to 5:00pm, with a 1.5 hour lunch break
Dates
21st April, 2025, 5 Mondays.
Cost
£390 + VAT.
30% discount if booked alongside another course.
Summary
More advanced R and omic analysis techniques, expands greatly on Block 1.
How to do stats in R, creating differential tables in a loop, power calculations, handling and exploring clinical data, survival curves, correcting omic data for batch effects, creating custom R functions, writing efficient omic analysis workflows, using PCA for biomarkers, k-means clustering, overlap analysis, signature analysis.
Essential Command Line Bioinformatics & Genome Analysis
Format
Requires Omic Data Analysis & Visualisation, Using R to be completed first.
Live Zoom lecture followed by a computer tutorial, using your own computer.
6 consecutive Mondays - 9:30am to 5:00pm, with a 1.5 hour lunch break
Dates
14th July, 2025, 6 Mondays.
Cost
£390 + VAT.
30% discount if booked alongside another course.
Summary
Command line data processing for genomics, epigenomics and transcriptomics.
The Linux environment, command line programming, writing pipelines, common bioinformatic tools (SAM tools, VCF tools, bed tools, bowtie2, star2, Kallisto, Macs), sequence QC, sequence alignment, calling polymorphisms, annotating and exploring mutations, multiple sequence alignment, phylogenetic trees, ChIP-seq peak calling, filtering and normalisation, annotations and visualising regions, splice aware alignment and read counting, DESeq2.