Analysis of High Dimensional Omics Data DL (3781)
  
Coordinating lecturer :Prof. dr. Ziv SHKEDY 
  
Member of the teaching team :dr. Rahma AZIZAH 

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 DL (3584) 4.0 stptn
    Concepts of Probability and Statistics DL (3220) 5.0 stptn
    Linear Models DL (3577) 5.0 stptn
 

Content

Microarray technology and High dimensional omics datasets, Data processing, Asymptotic versus resampeling based inference, Statistical analysis of high dimensional data, group comparisons, Multiplicity, Exploratory versus Supervise analysis, Class prediction and Classification, Statistical modeling of microarray data, linear mixed models and their use in the high dimensional data setting


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Project ✔  


Evaluation

Semester 1 (3,00sp)

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

 

Compulsory course material
 

Handouts
 

Recommended reading
  Exploration and Analysis of DNA Microarray and Protein Array Data,Dhammika Amaratunga; Javier Cabrera and Ziv Shkedy,Wiley,9780471273981,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=1602 916&pq-origsite=summon


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
second year Data Science - distance learning J
second year Master Bioinformatics - distance learning N
second year Master Biostatistics - distance learning J
second year Quantitative Epidemiology - distance learning J



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