BMB831: Biostatistics in R II

Study Board of Science

Teaching language: English
EKA: N210021102, N210021112
Assessment: Second examiner: External, Second examiner: None
Grading: 7-point grading scale, Pass/Fail
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N210021101
ECTS value: 5

Date of Approval: 18-04-2024


Duration: 1 semester

Version: Approved - active

Entry requirements

None

Academic preconditions

Students taking the course are expected to:

  • Have knowledge in statistics 
  • Understand the basic principles of molecular biology
  • Have basic programming skills in R
  • Know the fundamentals of biostatistics

Course introduction

Modern experimental platforms nowadays deliver the quantification of pools of biological molecules. Their analysis requires complex bioinformatics pipelines to obtain biologically relevant results. The students will use the acquired knowledge to design and apply work flows that handle omics data sets. The course consists of a theoretical and an extensive practical part, with the objective to provide advanced understanding of data analysis with R scripts and application of bioinformatics tools.

The course will introduce the students to advanced programming of R scripts necessary to deal with data from modern high-throughput experiments and gives a broad overview of tools for biological interpretation. Exercises involve in-depth application of standard pipelines to process omics data and a final project to apply the acquired abilities on real data that might come from experiments previously carried out by the student, e.g. during their bachelor/master thesis.

Expected learning outcome

The learning objectives of the course are that the student demonstrates the ability to:
  • independently analyze even conceptually demanding data sets. 
  • work with large data amounts and carry out standard statistical analysis to identify relevant features. 
  • use standard algorithms for multi-variate analysis
  • design scripts for detailed visualization of their results. 
  • know and apply tools for data interpretation.
  • know and apply standard pipelines for the processing of omics data.
  • know how to objectively discuss applied data analysis methods presented e.g. in publications.

Content

The following main topics are contained in the course:
  • statistics for large data sets
  • different types of data modeling
  • advanced data visualization
  • advanced data interpretation
  • computational tools for protein characterization
  • standard work flows for data from omics experiments

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn

Prerequisites

Type Prerequisite name Prerequisite course
Examination part Prerequisites for participating in the exam a) N210021101, BMB831: Biostatistics in R II

Tests

Individual report

EKA

N210021102

Assessment

Second examiner: External

Grading

7-point grading scale

Identification

Full name and SDU username

Language

English

Examination aids

To be announced during the course

ECTS value

5

Prerequisites for participating in the exam a)

Timing

Autumn

Tests

Tutorial and exercises

EKA

N210021112

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

English

Examination aids

To be announced during the course

ECTS value

0

Additional information

Participation at minimum 80% of tutorials
The prerequisite examination is a prerequisite for participation in exam element a)

Indicative number of lessons

46 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.
  • Intro phase (lectures) - 14 hours
  • Training phase: 32 hours, including 32 hours tutorials

The Intro phase consists of lectures where the students learn about the underlying concepts of R programming and biostatistics. They are accompanied by many hands-on examples and interactive slides to let the students improve and test their R and statistics skills during the lectures. 

The training phase involves solving a given set of exercises for each topic. These will be done during the exercises with the help of the instructors and should be completed as homework.

Teacher responsible

Name E-mail Department
Veit Schwämmle veits@bmb.sdu.dk Institut for Biokemi og Molekylær Biologi

Additional teachers

Name E-mail Department City
Jesper Grud Skat Madsen jgsm@bmb.sdu.dk Institut for Biokemi og Molekylær Biologi

Timetable

Administrative Unit

Biokemi og Molekylær Biologi

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.