Starter project · Beginner
Your first RNA-seq comparison
Use a small packaged airway count dataset to compare treated and untreated samples while accounting for cell line.
The question
Which genes show evidence of a treatment-associated expression change in this paired experimental design?
Before you begin: Basic R, count matrices, and introductory experimental design.
The data
The Bioconductor airway example dataset: four cell lines measured with and without dexamethasone. Starts from counts, not FASTQ files.
What you’ll make
- Sample metadata and a PCA plot
- A differential-expression table with effect sizes and adjusted p-values
- An MA plot and software session information
Work through it
- Read the sample metadata and explain why cell line belongs in the design.
- Run the supplied R script and inspect the PCA before interpreting differential expression.
- Choose three genes, report effect sizes and adjusted p-values, and state the limits of the comparison.
- Use the linked full RNA-seq workflow to learn how raw reads become counts.
Read alongside the project
- Bioconductor: gene-level RNA-seq workflow ↗
Connect RNA-seq processing with exploratory analysis and differential expression through a documented workflow.
Workflow · Intermediate · Free · English - DESeq2 vignette ↗
Understand count-based differential expression, model design, contrasts, diagnostics, and interpretation.
Documentation · Intermediate · Free · English