Biological data analysis

lboe2112  2020-2021  Louvain-la-Neuve

Biological data analysis
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
24.0 h + 36.0 h
Q1
Teacher(s)
Language
French
Prerequisites
In order to successfully follow this course, you should be acquainted with the concept of Probability and the
rules of Probability calculus, the bases of statistical inference, the principles and practice of the classical methods
for statistical analysis of continuous data (Regression, Analysis of Variance) and of discrete data (Contingency
tables, Goodness of fit tests), and the use of a statistical software for applying the above.
Main themes
Taking into account the most frequently encountered needs of researchers in Biology, as well as the time
constraints, the course offers of two main modules : Linear Modeling, and Methods of Multivariate Analysis.
The examples presented are mainly drawn from researches in Ecology.
Aims

At the end of this learning unit, the student is able to :

1 The objectives are that, as a result of successfully attending this course, the students :
' Are aware of the necessity of planning any scientific experiment before it is started.
' Have practiced, in the frame of a personal scientific question, the principles of experimental design.
' Are able to review, choose, and apply knowingly the best adapted methods for modeling and analysing
data from their domain of expertise in Biology.
' Are able to set up a scientific experiment, to manage the data generated by this experiment, to analyse
them (usually with the help of a computer software), and to interprete critically the results.
' Have shown their ability to report a scientific experiment in a written document and through an oral
communication. These reports may be elaborated in groups of two or three students.
 
Content
Module 1 (UCLouvain): Linear statistical modeling
– Simple and multiple linear regression, AN(C)OVA included
– Generalised linear models: logistic and Poisson regressionoisson
– Linear mixed models
– Implementation in R
Module 2 (UNamur): Multivariate data exploration
– Data matrices
– Useful techniques from matrix algebra
– Multiple linear regression (no inference)
– Principal component analysis
– Classification
– Canonical correspondence analysis
– Implementation in R and Excel
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Lectures and exercise classes in computer rooms.
For Module 2 (UNamur), self-learning sessions and flipped classrooms; instructions will be given in the first course hour.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Each of the two teachers will give a grade on 10 and this will grade will count for 50 % in the total. To succeed, the sum of the two grades should at least be 10/20 and each grade should at least be 4/10. Partial grades of 5/10 and higher are credited for the running academic year.
Module 1 (UCLouvain) : written exam during the exam session. Dispensatory test for a part of the exam near the end of the lectures.
Module 2 (Unamur) : Continuous evaluation during flipped classrooms (50%) : multivariate analyses in Excel and interpretation of the results. Evaluation during exercise classes (50%) : multivariate data analyses in R and interpretation of the results. No second session.
Online resources
Moodle page: https://moodleucl.uclouvain.be/course/view.php?id=7525
Module 1 (UCLouvain): R scripts of the recommended book: http://highstat.com/index.php/analysing-ecological-data
Module 2 (UNamur)
– Self-study website: http://webapps.fundp.ac.be/umdb/biostats2017/
– Videos:
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-200-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-210-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-20.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-220-30.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2016-cours-biostatistique-Depiereux/module-230-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-10.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-20.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-30.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-40.mp4
http://medias.save.fundp.ac.be/videos/webcampus/2017-cours-biostatistique-Depiereux/module-240-50.mp4
Bibliography
  • Dias cours magistraux, syllabus TP, bases de données, codes informatiques. Site web auto-apprentissage.
  • Alain F. Zuur, Elena N. Iono, Graham M. Smith, Analysing Ecological Data, Springer Science, 2007 (non-obligatoire)
Teaching materials
  • Dias cours magistraux, syllabus TP, bases de données, codes informatiques. Site web auto-apprentissage.
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Biology of Organisms and Ecology

Master [60] in Biology