whorlmap-paper

Analysis and figures for a technical paper documenting the design and application of whorlmap.

Whorlmap is a compact heatmap design for high-dimensional comparisons where each cell encodes a full bootstrap/effect-size distribution as a tiny spiral, rather than collapsing uncertainty into a single color. It lets readers scan many treatment–outcome contrasts at once while still seeing direction, magnitude, and uncertainty inside each cell.

Reproduce Figure 1

git clone https://github.com/sangyu/whorlmap-paper
cd whorlmap-paper/nbs
pip install dabest pandas numpy matplotlib seaborn scipy Pillow svgutils lxml nbconvert ipykernel
jupyter notebook figure1.ipynb

figure1.ipynb loads the committed expression cache (gtex_data/figure1_expr_cache.pkl.gz, 514 KB) and runs dabest.combine() live — bootstraps, whorlmap, and all panels are generated fresh. Runtime ~10–15 min.

Full reproducibility from raw GTEx data

See helpers/survey.ipynb for the full download and parse pipeline, then helpers/make_cache.ipynb to rebuild the expression cache from scratch.

Data

Gene expression data are from GTEx v8:

GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020). https://doi.org/10.1126/science.aaz1776

Repository layout

nbs/
  figure1.ipynb               main figure notebook
  helpers/
    survey.ipynb              GTEx download & parse pipeline (one-time)
    make_cache.ipynb          rebuild expression cache from gene_expr.pkl
  gtex_data/
    figure1_expr_cache.pkl.gz committed 514 KB expression cache
    sample_meta.csv           sample → region/sex mapping
    survey.csv                bootstrap statistics across all GTEx genes