BMB831: Biostatistics in R II

Study Board of Science

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

STADS ID (UVA): N210021101
ECTS value: 5

Date of Approval: 29-04-2019


Duration: 1 semester

Version: Approved - active

Comment

01014401 (former UVA) is identical with this course description. 

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 Blackboard for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)

Timing

Autumn

Tests

Tutorial and exercises

EKA

N210021112

Censorship

Second examiner: None

Grading

Pass/Fail

Identification

Student Identification Card

Language

Normally, the same as teaching language

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)

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

Censorship

Second examiner: External

Grading

7-point grading scale

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

5

Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

hours per semester

Teaching Method

In the individual report, the student describes how they solve a given biostatistics assignment. Details on the individual assignment will be discussed during the exercises. The report should consist of at least 5 pages with a maximum of 10 pages excluding references. It should contain the following sections: a) Introduction and description of assignment; b) Overview of the student's procedure to solve the task; and c) evaluation of results and conclusion.

Teacher responsible

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

Timetable

43
Monday
19-10-2020
Tuesday
20-10-2020
Wednesday
21-10-2020
Thursday
22-10-2020
Friday
23-10-2020
08 - 09
09 - 10
10 - 11
Class f
Forelæsning
Online
11 - 12
Class f
Forelæsning
Online
12 - 13
Class f
Forelæsning
Online
Class h1e
*Odense NAT Lokalitet aftales 2
Undervisning
Online
13 - 14
Class f
Forelæsning
Online
Class h1e
*Odense NAT Lokalitet aftales 2
Undervisning
Online
14 - 15
15 - 16
Show full time table

Administrative Unit

Biokemi og Molekylær Biologi

Team at Registration & Legality

NAT

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