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.
Note that this course requires prior knowledge of R. Places are extremely limited and will be offered based on the answers from the registration form.
What did our delegates have to say about this course?
Took the intermediate course at the beginning of lockdown and I can honestly say I am using R every day now (and having so much fun!)-recommend to anyone who has data to analyse. Dr Helen Wright, Career Development Fellow Versus Arthritis; Tenure Track Fellow, University of Liverpool.
Can’t recommend these courses and the support team enough! Fab accessible content that got this (former) R-phobic coding and growing in confidence. Dr Hannah Davies, Postdoctoral Research Associate, University of Liverpool.
Thanks to the fantastic @LivUniCBF 'R for data science' course... wish I'd done this years ago!. Dr Katharine Stott, Wellcome Trust Clinical Fellow
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