Reproducing the published analyses

Every figure and table in the manuscript comes from a shell script, and the scripts live in the benchmark repository rather than here. That split is deliberate and it is the one thing to understand before running any of them:

The command line designs and scores; the statistics live in the benchmark repository. mhcmatch cassette select chooses a set and mhcmatch cassette score prices it, and both are driven from the shell exactly as a user would drive them. A hazard ratio, an AUROC and a paired interval are statistics over those scores, and they stay in bench/cassette/*.py, because a library that shipped them would be shipping a cohort and an analysis alongside its model.

The three chains

script	what it rebuilds
bench/run_epic.sh	the EPIC fit and every head-to-head against a published pipeline
bench/run_cassette.sh	the observational cassette corpus: who is in it, their designs, and the TCGA arms — the hot/cold contest against mutational burden and the per-tumour-type survival read
bench/run_icb.sh	the checkpoint-blockade arm: one workbook flattened, every candidate scored, the clone partition, and the models against overall survival, progression-free survival and RECIST
bench/figures/fig*.sh	one script per manuscript figure, each driving the installed command line and writing the plot data beside the figure

Each takes --reuse to skip a stage whose output already exists, and each gates on the library version before it runs anything:

want=$(<MHCMATCH_VERSION)
have=$(python -c 'import mhcmatch; print(mhcmatch.__version__)')

A table stamped by one release and plotted against another is the defect that check exists to prevent: the numbers would look fine and would describe two different models.

What you need on disk

The chains fetch what they can. Reference data comes from the isalgo/pmhc_data deposit on Hugging Face and is staged by Staging reference data: the four tiers of bootstrap; the PanCanAtlas inputs are downloaded from the GDC by the UUIDs recorded in bench/cassette/sources.py, which is the citation as well as the address.

Two inputs cannot be fetched, and both stages skip loudly rather than failing when they are absent: the publisher supplements for the vaccination trials, and the supplementary workbook of the checkpoint-blockade cohort. Raw sequencing for that cohort is under controlled access and is never touched — nothing in the chain needs it.

Reproducing one patient’s report

The smallest end-to-end run needs no cohort at all:

mhcmatch cassette select --candidates pool.tsv -k 20 --out design.tsv
mhcmatch cassette score  --cassettes design.tsv --pool pool.tsv --out scored.tsv
mhcmatch cassette report --cassettes design.tsv --pool pool.tsv --out report.html

Add --reference cohort.tsv --reference-column lam --reference-outcome os_months to the last command and the report gains a risk band placing this patient against that cohort. The cohort is yours to supply; see Designing a cassette: what goes in, and what it is worth.