This introductory course gives an overview of many statistical tools to analyse omics data. The course can be followed by researchers with a minimum or elementary background in quantitative data analysis (see ‘Pre-requisites’ below).
Participants will learn and practice commonly used tools including:
- Tools to explore datasets including clustering, principal components and network analysis;
- Models to answer basic statistical questions: differential behaviour (e.g. mRNA expression) and multiple testing, also using Bayesian models;
- Models for classification and prediction, including penalised regression;
- Models for emerging technologies: radiomics and single-cell sequencing data.
Methods will be applied on experimental data in practical hands-on sessions using the statistical software R. Insight about how methods work is given in an intuitive way wherever possible which, combined with some formalisation and the practical work, makes theory accessible and helps cement concepts. Slides and instructions for the practical sessions will be made available electronically to participants.
Pre-requisites: Participants are assumed to be familiar with the following at the start of this course:
- Basic R, including how to use functions and packages;
- Basic statistics: mean, variance, standard deviation;
- Probability distributions: normal;
- Statistical testing: t t -test, Wilcoxon test, significance level, p p -value, null & alternative hypotheses;
- Measures of association: Pearson correlation, regression.
For whom?
