Preamble

The main objective of this course is to demonstrate how to proceed with a complete analysis of RNA-seq data, including principal component analysis, identification of differentially expressed genes and gene set enrichment and over-representation analysis.

The background for these analyses inludes introductions to hypthesis testing, linear modelling and adjustment for multiple comparisons, principal component analysis and high throughtput sequencing.

This course is based on material from the WSBIM1322 and WSBIM2122 chapters taught at the Faculty of Pharmacy and Biomedical Sciences at the UCLouvain.

Installation instructions

pks <- c("tidyverse", "patchwork", "remotes",
         "SummarizedExperiment", "DESeq2",
         "AnnotationDbi", "factoextra", "pRolocdata",
         "org.Hs.eg.db", "GO.db", "msigdbr",
         "clusterProfiler", "airway",
         "UCLouvain-CBIO/rWSBIM2122",
         "UCLouvain-CBIO/rWSBIM1322")

if (!require("BiocManager"))
    install.packages("BiocManager")

BiocManager::install(pks)

Data preparation

To prepare soem of the data needed in this course, run rWSBIM2122::prepare_shell() as shown below:

rWSBIM2122::prepare_shell()
## Preparing shell data in 'wsbim2122_data'.
## wsbim2122_data is ready.

The function creates a new directory called wsbim2122_data containing the following sub-directories:

dir("wsbim2122_data")
## [1] "count_data"      "data"            "deseq2"          "RNAseq_pipeline"

References

  • References are provided throughout the workshop material.

  • Modern Statistics for Modern Biology, by Susan Holmes and Wolfgang Huber (Holmes and Huber 2019Holmes, Susan, and Wolfgang Huber. 2019. Modern Statistics for Modern Biology. Cambridge Univeristy Press.). A free online version of the book is available here.

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