Statistical Analyses of -omics Data

lstat2340  2025-2026  Louvain-la-Neuve

Statistical Analyses of -omics Data
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5.00 credits
30.0 h + 10.0 h
Q2
Language
French
> English-friendly
Prerequisites
Concepts and tools equivalent to those taught in teaching units
LSTAT2020Logiciels et programmation statistique de base
LSTAT2110Analyse des données
Main themes
  • Introduction to omics data (definitions, reasons for generating them, examples)
  • Statistical characteristics of omics data (type and nature of data, typical distributions, etc.)
  • Correction methods for multiple tests
  • Details of the most common experimental protocols and methods for pre-processing and analysis of omics data (transcriptomics, metabolomics, proteomics, metagenomics, flow cytometry, and single-cell transcriptomics)
  • Reviews of supervised (classification and regression, PLS(-DA), O-PLS, Lasso & ridge regression, SVM) and unsupervised (PCA, MDS, clustering) multivariate analysis methods and variance component models (ASCA, APCA).
  • Data integration methods (multitable data analysis)
  • Mathematical and statistical methods for spectral data pre-processing (e.g. semi-parametric smoothing models for baseline correction, peak alignment).
  • Methods for correcting batch effects and experimental planning to avoid them.
  • Review and use of R packages for omics data analysis (typically BioConductor packages).
  • Application to real data.
Learning outcomes

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

1 With regard to the AA referential of the “Master in Statistics, Biostatistics Orientation” program, this activity enables students to master
  • As a priority, the following AA: 1.4, 1.6, 2.2, 2.4, 2.5, 2.6, 3.1, 3.2, 3.3, 3.4, 3.5, 4.3, 4.5, 5.4, 5.6
  • Secondary AAs: 5.2, 5.7
With regard to the AA referential of the “Master in Statistics, General Orientation” program, this activity enables students to master
  • Priority: 1.4, 1.6, 2.2, 2.4, 2.5, 2.6, 3.1, 3.2, 3.3, 3.4, 4.3, 5.3
 
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,
  • 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://moodle.uclouvain.be/course/view.php?id=2964
Faculty or entity
> LSBA


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

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic
DATS2M
5
Master [120] in Statistics: Biostatistics
BSTA2M
5
Master [120] in Statistics: General
STAT2M
5
Master [120] in Chemistry and Bioindustries
BIRC2M
5
Certificat d'université : Statistique et science des données (15/30 crédits)
STAT2FC
5
Master [120] in Agricultural Bioengineering
BIRA2M
5