Bulk RNASeq: from counts to differential expression
This 2-day course consists of e-learning on counting, showing tools to generate count files (e.g. FeatureCounts, HTSeq Count) and how these count files are used to identify differentially expressed genes. The course also offers two face-to-face sessions on differential expression analysis in R and all the questions that arise when analysing your own data.
Knowledge of NGS data formats and the first steps in the analysis workflow advised (fastqc -> bam files). If you are a newbie in the field, you should attend the NGS introduction training first.
Experience in R programming advised. If you have never worked in R you should attend the R introduction training first.
Counting using Galaxy: STAR, htseq-counts, FeatureCounts
Identification of DE using Bioconductor: DESeq2 + other packages like tximeta (script for EdgeR is provided but not demonstrated)
Visualization of results using R: ggplot2, pheatmap,
Mapping of IDs to Gene symbols using Bioconductor: AnnotationDbi