Aim and content

Large-scale data analysis of “-omics” data in the biological sciences.
Many experimental procedures such as the various “-omics” techniques routinely employed within biotechnology/biological research fields produce vast amounts of data. Therefore, the amount of available data in many biological disciplines is steadily increasing. Fundamental knowledge and skills of large-scale computing systems and analysis methods is required to make use of this wealth of information. The purpose of this course is to introduce the theory and practice of large-scale data analysis to students, which will allow them to perform and assess different types of ”-omics”-scale data procedures, specifically focusing on Transcriptomic data (RNAseq) and Metabolomic data (LC-MS).

Formel requirements

Basic statistical understanding equivalent to a MSc from SCIENCE; Beginners level experience with R
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Completion of the course will rely on the production and acceptance of a complete data analysis report in Rmarkdown.

Learning outcome

Learning outcome

Knowledge:
•The general principles of large-scale data analysis
•Common pitfalls in large-scale data analysis
•The basic concepts underlying clustering and visualization techniques

Skills:
•How to efficiently keep, move, and analyse large amounts of data
•How to structure and perform large-scale data analyses in a coding-based software environment, such as for example R
•Handling and modifying large datasets
•Visualization and dissemination of data

Competences:
•Analysing different types of large-scale biotechnology data
•Critically evaluating the quality of different types of biotechnology data
•Assessing and understanding results of large-scale data analyses

Literature

Original literature, software manuals and tutorials, and teacher provided compendia

Target group

All phd-students within biology, biotechnology, medicine, pharmaceutical sciences etc.

Teaching and learning methods

Lectures and computer exercises.
Completion of the course will rely on the production and acceptance of a complete data analysis report in Rmarkdown.

Remarks

UCPH discloses non-sensitivepersonal data to course leader/speakers, if any. In addition, we will disclosenon-sensitive personal data to the other participants in the course.
Non-sensitive personal data includes names, job positions, institution names& addresses, telephone numbers and e-mail addresses.

Disclaimer:
DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.