Day 2 · Module 610:10 – 11:20·70 min

RNA-seq: Quantification + Differential Expression

HISAT2, featureCounts/Salmon, and DESeq2 for differential gene expression.

What this module covers

▸HISAT2: splice-aware alignment
▸featureCounts vs Salmon: read counting vs quasi-mapping
▸R/DESeq2: normalization, dispersion estimation, Wald test
▸Volcano plots, MA plots, heatmaps
▸Gene ontology enrichment (clusterProfiler)

Download .ipynb RNA-seq pipeline (bash)DESeq2 analysis (R script)RNA-seq engine (Python script)DESeq2 results (TSV)

The notebook — live & editable

runs in your browser · no install

Every section's code is already filled in below. Press the ▶ next to any cell (or Shift+Enter) to run it, edit it and run again, or hit Run all to execute the whole notebook top to bottom. No Python or Jupyter install needed — the kernel boots right here in your browser.

Python kernel — not started

first run downloads the runtime (~once, a few seconds)open in full Jupyter ↗

Heads up: this module's pipeline uses command-line tools (e.g. bwa, samtools) that aren't available in the browser kernel. The Python cells run here; tool/shell lines print a note instead.

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