Bulk RNASeq: from counts to differential expression
General context
The course consists of a live session on counting and differential expression analysis in R and a Q&A session to answer all the questions that arise when trying the analysis on your own data
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The course will show:
- R tools to generate count files like featureCounts, and summarizeOverlaps are demonstrated
- Count files from HTSeq-Count, FeatureCounts, Salmon or Kallisto are used to identify differentially expressed genes
After the live session participants can analyze their own count files. Issues can be handled during the Q&A session.
Experience in basic R programming. If you never worked in R you should attend the Basic statistics in R training first.
- Counting using Bioconductor: Rsubread - GenomicAlignments
- 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
Trainers
Janick Mathys
Janick tries to help VIB scientists analyze their data by offering bioinformatics training and support. Next to organizing trainings, creating e-learning courses and teaching statistics, R, Python, Linux, HPC, bulk and single cell RNA-Seq analysis, she consults scientists and develops pipelines for omics analyses. Before joining VIB, she worked as a post-doc at KULeuven, doing research on transcriptomics and transcription regulation. She coordinated the Master of Bioinformatics program of KULeuven and taught the course on Biological Databases.