Statistical and Computational Methods for Integrated Analysis (1756)
  
Coordinating lecturer :Prof. dr. Ziv SHKEDY 
  
Co-lecturer :Prof. dr. Willem TALLOEN 

Language of instruction : English
Credits: 3,0
  
Period: quarter 3 (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
    Concepts of Probability and Statistics (1798) 5.0 stptn
    Linear Models (3560) 5.0 stptn
 

Content

Asymptotic versus resampeling based inference.

Permutations and bootstrap tests, resampeling methods, multiplicity, familywise error rate, false discovery rates, integrated analysis, and analysis of high dimensional data.

Software development for high dimensional data


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  


Evaluation

Quarter 3 (3,00sp)

Evaluation method
Oral exam50 %
Open questions✔
Presentation✔
Other exam50 %
Other Written project.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

Handouts and papers.
 

Recommended reading
  Modeling Dose Response Microarray Data in Early Drug Development Experiments Using R: Order Restricted Analysis of Microarray Data,Lin D.; Shkedy Z.; Yekutieli D.; Amaratunga D.; Bijnens L.,1,Springer Verlag Berlin Heidelberg,9783642240065,Available as e-book: https://link.springer.com/book/10.1007%2F978-3-642-24007-2

Expl oration and Analysis of DNA Microarray and Protein Array Data,Dhammika Amaratunga; Javier Cabrera,Wiley,9780471273981


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student can put research and consulting aspects of one or more statistical fields into practice.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

  •  EC 

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

  •  EC 

    • The student is capable of acquiring new knowledge.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics J
2nd year Master Bioinformatics - icp 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.