Bulk RNASeq: from counts to differential expression
The course consists of introductory online material (e-learning) on counting and two face-to-face sessions on differential expression analysis in R and all the questions that arise when trying the analysis on your own data.
The course will show:
- Tools to generate count files like featureCounts, and htseq count are demonstrated
- Count files from HTSeq-Count, FeatureCounts, Salmon or Kallisto are used to identify differentially expressed genes
On the second day, participants can analyze their own count files. Issues are solved and questions are answered by experts from the VIB Nucleomics Core.
- 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