Teaching

2012-2020 Statistical analyses of RNA-seq data. Interpretation of gene lists and gene network inference, courses/TD (20h), researchers, Agrocampus Ouest Rennes,  website of the ‘Omic & NGS – Rennes‘ workshop

Description: RNA-seq normalization and differential analysis using Deseq2/edgeR. Splicing analysis. Functional annotation databases, enrichment test for the characterization of gene lists of interest, gene network inference methods (WGCNA and Glasso package). Application to several real datasets, use of R software.

2017-2020 Statistical analysis of genomic data, course/TD (8h), M1-M2, Université Paris Descartes

Description: Introduction to high throughput technologies, overview of classical data preprocessing methods and statistical methods for the search of differentially expressed genes (Student /limma test, correction for multiple tests), use of R software.

2009-2013 General statistics, TD (48h/year), L3-M1, Agrocampus Ouest Rennes.

Description: descriptive statistics, statistical inference (estimation, hypothesis tests, confidence intervals), variance analysis, simple linear regression; introduction to R software.

2012-2013 Linear model and data analysis, TD (48h), L3-M1, Agrocampus West Rennes

Description: multiple linear regression, analysis of variance, experimental design, exploratory analysis (PCA); use of FactoMineR package in R software.

2012-2013 Statistics and decision support, TD (18h), M1-M2, Agrocampus Ouest Rennes

Description: categorical data modeling (χ test, logistic regression, decision trees); TD with R software.

2012-2013 Biostatistics and introduction to the R language, courses/TD (17h), M1, University of South Brittany, Lorient

Description: Introduction to high throughput technologies, overview of classical data preprocessing methods and statistical methods for the search of differentially expressed genes (student /limma test, correction for multiple tests). Introduction to R software.

2010-2012 Statistical analysis of transcriptomic data, course/TD (10h), L3, Roscoff Biological Station.

Description: Introduction to high throughput technologies, overview of classical data preprocessing methods and statistical methods for the search of differentially expressed genes (student /limma test, correction for multiple tests), use of R software.

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