Teaching
I am passionate about enabling all scientists to work on functional multidisciplinary teams. For this we need to broaden the horizons of our molecular biology students and develop them in aspects of statistics, programming and overall data science. I am personally engaging in different activities and workshops to provide support to students using my expertise in systems biology and overall computational biology.
I also supervise post-graduate students on computational biology projects and research.
R for beginners
R for beginners - CBF course I lead this 3-day CPD course which was originally developed by both the CBF and the CGR (Centre for Genomic Research) at the University of Liverpool. The contents covered include: Day 1: R basics: structures, functions and data manipulation Day 2: Visualisation: beautiful plots ready for publication Day 3: Introduction to Statistical analyses in R (including univariate tests and Principal Component Analysis) Extra materials: introduction to the Tidyverse The course has been designed to introduce R from the very basics. Therefore applicants do not need any prior experience to attend. Real life examples with bioinformatic applications are included. Have a look to the next iterations and testimonials here.
R for data science applied to life sciences
R for Data Science - CBF course The course covered content for these learning objectives: To write your own custom functions To use for loops, apply functions and if statements To analyse large matrices of data in a semi-automated way To normalise data To quantify and correct batch effect To undertake the most common clustering algorithms including k-means and hierarchical To perform variable selection and present these results in different plots including heatmaps To do 2-way ANOVA To undertake multivariate modelling A brief introduction to machine learning. A brief introduction to functional enrichment with R using the package clusterProfiler The course included brief theoretical introductions followed by hands on exercises based on real life research examples. The first two days of the course focus on learning programming concepts that would allow the delegates to speed up their anlayses and build their own custom functions. This follows other two days of hands-on exercises covering the typical research pipelines from normalisation to functional enrichment. More information and testimonials from delegates here.
Statistics for NMR metabolomics
Statistics for NMR metabolomics - CBF & NMR metabolomics course This course runs annually tipically in January/February. This course is of interest to to anyone who is undertaking or planning to undertake analysis of metabolomics data or is keen to refresh concepts about considerations when analysing -omics datasets. The course covers normalisation, basic univariate and multivariate analysis employing NMR metabolomics programs, bespokely developed in-house by the Computational Biology Facility, for use in the programming language R. The course has been designed such as no prior knowledge of R is required although it can be beneficial.
Modules for 2024-25
BIOINFORMATICS RESEARCH SKILLS
Module code: LIFE748
Role: Teaching
Computational Biology
Module code: LIFE752
Role: Teaching
MSC RESEARCH PROJECT
Module code: LIFE703
Role: Teaching