Analysis of Microbiome Data (4565)
  
Coordinating lecturer :Prof. dr. Olivier THAS 
  
Co-lecturer :Prof. dr. Ziv SHKEDY 
  
Member of the teaching team :Mevrouw Thi Huyen NGUYEN 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Bioinformatics (3566) 4.0 stptn
    Linear Models (3560) 5.0 stptn
 

Prerequisites

The student masters the basics of statistical inference and probability, linear models, basics of multivariate and high dimensional data analysis, multiple hypothesis testing and FDR control, and programming in R.


Content

The student will learn about state-of-the-art statistics and bioinformatics methods for the analysis of microbiome studies.

The following topics are included:

  • what is the microbiome
  • from sequencing reads to OTU and ASV tables
  • data characteristics: overdispersion, sparseness, compositionality, ...
  • data exploration and visualization: barplots, PCoA plots, RCM plots
  • diversity indices (alpha and beta diversity)
  • testing for differential abundance
  • advanced modelling of microbiome data (e.g. longitudinal data analysis)
  • advanced case studies (example of intervention microbiome experiments, basic settings and differential abundance across intervention levels and ecosystem of microbiome (OTUs, alpha diversity, family level etc))
  • advanced modelling of microbiome data (High dimensional microbiome biomarkers)


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  
Teaching methods  
Paper ✔  
Presentation ✔  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written evaluation during teaching period50 %
Transfer of partial marks within the academic year✔
Paper✔
Oral exam50 %
Open questions✔
Presentation✔
Use of study material during evaluation✔
Explanation (English)The student may bring a copy of her/his own paper (including appendices and R code) and her/his slides for the presentation to the exam.
Evaluation conditions (participation and/or pass)✔
Conditions The student must have submitted the paper in time.
Consequences If the paper is not submitted or is not submitted in time, the student will fail for the course.

Second examination period

Evaluation second examination opportunity different from first examination opprt
Yes
Explanation (English)If the student received a pass mark for the papers in the first chance
exam period, the student may keep these partial marks and in the second
chance exam the student must present the paper and will receive
questions related to the papers (just like for the first chance exam).
If the student did not receive a pass mark for the papers in the first
chance exam period, the student will receive new project assignments.
 

Compulsory course material
 

Lecture notes or slides will be made available on Blackboard. 

The software R will be used in this course. 


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

    • The student is able to efficiently acquire, store and process data.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective oral communicator in their own field.

     
  •  DC 

    • The student is an effective oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer in their own field.

     
  •  DC 

    • The student is an effective writer, both within their own field as well as across disciplines.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

  •  EC 

    • The student knows the societal relevance of statistics and data science.

     
  •  DC 

    • The student can reflect on and explain the societal relevance of a task, particularly within the programme specialization

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics J
2nd year Master Bioinformatics - icp J
2nd year Master Biostatistics J
2nd year Master Data Science J
2nd year Master Quantitative Epidemiology J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.