Quantitativ Biology and Integrative Biology 7.5 credits

About the course

The course focuses on basic mathematical and statistical methods that are widely used within modern bioinformatics. The course starts with linear algebra (matrices, vectors, eigenvalues) that are then applied to markov chains (observable and hidden). The wide utility of linear algebra is studied through testing alternative bases, with examples from, e.g., image and signal analysis. To be able to study high dimensional data, the course also introduce coordinate transformations and dimensional reduction (PCA and OLS).
The course introduced Bayesian statistics and solved related complex equations (e.g., RSTAN). The focus is on statistical models common within bioinformatics (e.g., the poisson and negative binomial distribution). Common statistical models for analyzing large dimensional data will be introduced. This includes multilinear models and nonlinear latent space models. This will be exemplified by applications in chemometrics and single cell analysis.