Bioinformatiikan matemaattiset menetelmät (MATLB0121), 3 op

Basic information

Prerequisites

Basic courses in mathematics (MATLB0111 and MATLB0112), basic course in statistics (MATLB0005)

Course contents (core content level)

Matrix algebra and multivariate spaces. Mathematical description of multivariate biological measurement and image data, e.g. gene expression data. Statistical methods for analyzing multivariate data. Principal component analysis (PCA). Clustering methods. Pretreatment of data.

Course contents (additional)

Self-organizing maps and other advanced methods.

Core content level learning outcomes (knowledge and understanding)

The student achieves a deeper understanding of the mathematical nature of genetic information. The student knows mathematical and statistical methods that can be used in analyzing and interpreting modern biological measurement data, e.g. gene expression microarray data.

Core content level learning outcomes (skills)

The student can use statistical tools in detecting significant differences or dependencies in multivariate biological data. The student is able to design experiments for gene expression microarray data. The student is able detect clusters in multivariate data either by PCA or clustering algorithms. The student is able to perform the most common data pretreatments.

Recommended reading

S. Draghici, Data Analysis Tools for Microarrays, Chapman & Hall/CRC
T. Speed (ed.), Statistical Analysis of Gene Expression Microarray Data, Chapman & Hall/CRC

Teaching and learning strategies

Class room teaching: 14 h
Computer labs: 21 h
Project: 15 h
Exam: 3h
Student individual workload (workload analysis not carried out): 27 h
Total: 80 h
Follow-up of the student workload analysis performed: -

Teaching methods and student workload

Assessment weighting and grading

Exams (minimum 40% of the maximum score), approval of the project.

Related competences of the degree programme

Theoretical basis and mathematical and science skills