Visualization + Pathway Analysis
IGV, matplotlib, Biopython, and pathway-level interpretation.
What this module covers
- ▸IGV deep dive: tracks, custom annotation
- ▸Python: matplotlib/seaborn genomic plots
- ▸Biopython: parsing sequence files programmatically
- ▸KEGG / Reactome pathway visualization
Start here — the data journey
live in your browser · no installWatch the data move through the pipeline below, then read on — each section has its own interactive explorer embedded right where the code builds that figure, so you can turn the knobs as you go.
The data journey — one dataset, five figures
1. Aligned reads. Millions of short reads pile up along the genome. Their depth at each base is the coverage track.
2. Count matrix. Reads per gene per sample become a genes × samples table — the raw material for everything downstream.
3. Normalize. Library-size scaling (log₂ CPM) then per-gene z-scoring puts every gene on a comparable scale.
4. PCA. Project samples into 2-D: replicates of the same condition cluster, the treatment axis separates groups.
5. Differential expr.. Per-gene stats give a fold-change and a p-value — the volcano plot summarises who moved and how surely.
6. Pathways. Significant genes map onto biological pathways — turning a gene list into a story about cell biology.
The notebook — live & editable
runs in your browser · no installEvery 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.