Biostatistics analysis of RNA-Seq data
Biostatistics analysis of RNA-Seq data
This course is part of the INRAE training session about “bioinformatics and biostatistics analysis of RNA-seq data” and of the Biostatistics platform “Initiation à LA statistique, niveau 4”. The previous sessions were held in Toulouse on November 18-21, 2014, April 2-3, 2015, September 23-24, 2015, May 18-19, 2017, February 11-12, 2019, November 4-5, 2020, April 14-15, 2021, April 13-14, 2022, and March 29-30, 2023. The next session is scheduled on May 16-17, 2024.
The material provided on the present webpage is related to the biostatistics part and covers the following topics:
- R and RStudio
- design of experiments
- variability
- count data normalization
- differential analysis
The material has originally been prepared by Ignacio Gonzales, Annick Moisan and myself. The class has already been taught by these persons but also by Gaëlle Lefort and Jérôme Mariette.
Pre-requisites: A background in R programming is necessary for this class. Before the class, please download the course material and install R, RStudio and the packages as described below. To produce high quality figures, I will use ggplot2 for plots but will not enter into details about the ggplot2 syntax. If you are not familiar with it, you can just use these command lines or switch to base plots instead. Do not hesitate to contact me before the class if you have any difficulty to install one of these packages!
Material
- (theoretical part) slides
- practical application: material, including a subdirectory
data(with datasets) and an Rmd (source) file. This file can be compiled within RStudio and will provide an HTML file of the complete analysis.
Required installation for the practical application
R is needed for the practical application and RStudio is strongly advised. If possible, use R version 4.0 or higher. Detailed installation instructions are provided on this page.
CRAN packages
ggplot2gridExtrareshape2RColorBrewerVennDiagramdevtoolsplotly
These packages can be installed using the following command line in an R console:
install.packages(c("ggplot2"))(and similarly for the other packages).
Bioconductor packages
mixOmicsedgeR
These packages can be installed using the following command lines in an R console (after the CRAN package BiocManager has been installed as above with the install.packages function):
BiocManager::install("mixOmics")(and similarly for the other packages).
The precise versions of R and of the packages used in the practical application are given at the end (“Session information” section) of the solution of the analysis (this HTML file).
Additional (older and not updated) material
- tutorial (PDF)
- practical application (PDF) and data (data are provided as a .tar.gz file that must be uncompressed in the R working directory)
edgeR: solution (HTML) and solution (Rmd) (.Rmdfiles can be compiled in RStudio with the R package knitr)edgeR(again…): dataset (tomatoes, 2 conditions) and design, solution of the analysis (in HTML format), R script and Rmd (source) fileDESeq2: solution (HTML) and solution (Rmd) (.Rmdfiles can be compiled in RStudio with the R package knitr)- global solution (PDF)
