English title:
Efficient microarray data analysis using R and FSPMA
Course ID:
790391
ECTS credits:
1,0
Title in native language:
Efficient microarray data analysis using R and FSPMA (in Eng.)
Efficient microarray data analysis using R and FSPMA (in Eng.)
Term Semester:
Autumn/Winter
Instruction language(s):
English
Course content:
The theory part of the lecture includes the following topics:
1) microarray normalization,
2) mixed effects ANOVA models,
3) ANOVA tables,
4) experimental designs,
5) specifying contrasts for comparisons,
6) adjustments for multiple comparisons,
7) setting up FSPMA for analysis of single and two color arrays,
8) visualizations of microarray data with FSPMA
9) combining FSPMA with external libraries.
The practical part of the lecture gives students the opportunity to analyse two publicly available microarray data sets for differentially expressed genes. The exercises cover all aspects of microarray analysis listed above and will in addition highlight the effects of altering analysis steps like the chosen normalization method. The practical work constitutes an essential part of the evaluation. The overall assessment is based on a laboratory protocol which we will discuss in a group session to determine the final mark.
1) microarray normalization,
2) mixed effects ANOVA models,
3) ANOVA tables,
4) experimental designs,
5) specifying contrasts for comparisons,
6) adjustments for multiple comparisons,
7) setting up FSPMA for analysis of single and two color arrays,
8) visualizations of microarray data with FSPMA
9) combining FSPMA with external libraries.
The practical part of the lecture gives students the opportunity to analyse two publicly available microarray data sets for differentially expressed genes. The exercises cover all aspects of microarray analysis listed above and will in addition highlight the effects of altering analysis steps like the chosen normalization method. The practical work constitutes an essential part of the evaluation. The overall assessment is based on a laboratory protocol which we will discuss in a group session to determine the final mark.
Expected previous knowledge:
The successful completion of the lecture depends on a basic knowledge of statistics and requires proficiency with using computers.
Learning outcomes:
Successful completion of the lecture enables students to analyse microarray experiments efficiently with the statistics package R and the FSPMA library. Participants will understand the theoretical concepts of statistical testing and ANOVA models in particular. Students will furthermore be able to analyse simple microarray experiment designs with R and provide useful graphical visualizations. Successful participants will in addition be able to independently familiarize themselves with sophisticated data analysis libraries such as for example provided in the Bioconductor archive.
Teaching and learning methods:
The lecture (VU) will be evaluated by assessing the laboratory protocol and finalised in a discussion of the achieved results.
Exam method:
The lecture (VU) will be evaluated by assessing the laboratory protocol and finalised in a discussion of the achieved results.
Organisation: University of Natural Resources and Life Sciences Vienna
Country:
Austria
Acronym:
BOKU
ERASMUS+ code:
A WIEN03
Teaching period summer semester:
22. Feb 2021 - 30. Sep 2021
Teaching period winter semester:
13. Oct 2020 - 21. Feb 2021
University website: