Course objective: Introducing statistical methods for high-throughput gene expression data analysis focusing on the search for genes whose expression is related to variations in experimental covariates.
Organization: The course is a one-day tutorial dedicated to the selection of genes whose expression depends on experimental conditions using RNA-Seq data. It introduces to the R packages DESeq2 and limma.
Organization: The course is divided into four sessions. Each session is made of a lecture and practical classes. In those in-class practical classes, students are given the opportunity to tackle problem-solving exercises, with or without the use of R.
The following documents are provided for each session:
To start with Rmarkdown: brief introduction.
Objective: Introducing general principles of gene expression data normalization. Normalization is a preliminary step in genomic data analysis consisting in identifying and removing variations only due to technological biases.
Objective: Being able to use the R package limma to import gene expression data into a R session and control the quality of the gene expression measurements.
Objective: Controlling false positives when selecting genes whose mean expression is significantly related to experimental covariates.
Objective: Using clustering procedures to give more insight to the list of selected genes. This short session is dedicated to clustering methods in order to extract groups of microarrays whose expression profiles are similar and groups of co-expressed genes.