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Notebook Workflows

The CLI should remain the primary interface, but notebooks are useful for eval review and result exploration.

Use notebooks as consumers of CLI output and report artifacts, not as the primary source of truth. Scripts and Rust code remain responsible for reproducible eval generation.

A typical workflow:

genesets-rs run evals/expression20/config.yaml

Then inspect evals/expression20/generated/results.tsv in a notebook with pandas, Polars, DuckDB, or plotting libraries.

For larger temporal reports, prefer a parameterized report config:

genesets-workflows go-impact evals/go_impact_5y_expression500.yaml

The notebook should show the report parameters and generated artifacts, then query the Parquet outputs.

The committed demonstrator notebooks live in notebooks/ and are linked from the Tutorials chapter:

  • 01_cli_quickstart.ipynb;
  • 02_expression20_eval.ipynb;
  • 03_go_diff_with_duckdb.ipynb.

Python Notebook

Python notebooks are the most portable option for collaborators:

  • read TSV outputs with pandas or Polars;
  • plot p-value distributions;
  • compare ranks across ontology releases;
  • join results to source metadata.

The demonstrator notebooks should use %%bash for CLI commands and Python for analysis only. They should not expose Rust APIs.

Repository Policy

Commit small, deterministic notebooks only when they add durable explanation. Keep outputs cleared or tiny, and write generated data under notebooks/generated/, ignored eval generated/ directories, or external artifacts.