Teacher(s)
Language
French
Prerequisites
Concepts and tools equivalent to those taught in teaching units
| LSTAT2020 | Logiciels et programmation statistique de base |
| LSTAT2110 | Analyse des données |
Content
After reviewing the basics of molecular biology, the course presents a series of -omics methods and especially related data processing methods:
- Molecular biology basics.
- Revision of multivariate methods useful in -omics methods (PCA, Clustering...) and application in R + RMarkdown.
- Transcriptomic data acquisition method (micro-arrays, q-PCR...).
- Pretreatment and analysis of transcriptomic data (background correction, normalization,.... + hypothesis tests with multiplicity correction).
- Use of prediction and classification models from chemometry and machine learning for the analysis of omic data (PLS, O-PLS, trees...).
- Acquisition and processing of proteomic data.
- Acquisition and processing of metabolomic data (including detailed pre-processing of 1H-NMR data).
- Processing of metagenomic data.
Teaching methods
The course consists of a series of activities that lead the student to actively immerse himself in the world of -omics data. It proposes:
- presentations by specialists active in the field,
- mini-projects of data processing to be carried out each week,
- interactive computer work during the course,
- a laboratory visit,
- a final project on data proposed by the various participants in the course or data repositories.
Evaluation methods
In this course, students are evaluated in two ways:
- continuous assessment including:
- mandatory assignments to be delivered during the quarter according to a schedule set at the beginning of the quarter (40% of the final grade)
- and a final project to be presented during the last class (40% of the final grade)
- an open-book oral exam (20% of the final grade)
Online resources
Moodle Site: https://moodleucl.uclouvain.be/course/view.php?id=10846
Faculty or entity
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic
Master [120] in Statistics: Biostatistics
Master [120] in Statistics: General
Master [120] in Chemistry and Bioindustries
Certificat d'université : Statistique et science des données (15/30 crédits)
Master [120] in Agricultural Bioengineering