Configuration Reference

IsoGraph uses dataclass-based typed configuration models in isograph.workflow.config.

Command Configs

  • BenchmarkCommandConfig Controls suite generation, backend selection, report locations, real-data freeze settings, and backend-specific config blocks. The default backend is "vae".

  • FitCommandConfig Controls baseline fitting for a prepared dataset bundle.

  • CompareCommandConfig Controls report or snapshot comparison output paths.

Backend Configs

  • BaselineModelConfig Sparse partial-correlation baseline with residualization and trait-association defaults.

  • LatentModelConfig Factor Analysis denoising plus partial-correlation inference. Supports cross-validated or fixed component count selection.

  • GraphModelConfig Latent model extended with graph-Laplacian smoothing.

  • VaeModelConfig Variational autoencoder backend with early stopping, latent-dimension controls, and optional checkpoint output. See the hidden_dim docstring for gene-count guidance.

  • WgcnaModelConfig WGCNA backend wrapping R’s blockwiseModules. Configures soft-thresholding power, minimum module size, merge cut height, network type, and subprocess timeout.

Real-Data and Stability Configs

  • RealDataFreezeConfig Points at the BrainSeq-style count and annotation tables used by freeze-real.

  • StabilitySelectionConfig Controls alpha-grid search for real-data edge stability.

Default Config Files

The repository ships with these YAML entry points:

File

Suite

Backend

configs/benchmark.yaml

core_v1

vae (default)

configs/fit.yaml

—

baseline

configs/compare.yaml

—

—

configs/stage3_graph.yaml

core_v1

graph

configs/stage4_vae.yaml

core_v1

vae

configs/stage5_wgcna.yaml

core_v1

wgcna

configs/stage6_vae_xlarge.yaml

scale_v1

vae

configs/stage6_scale_comparison_vae.yaml

scale_v1

vae

configs/stage6_scale_comparison_wgcna.yaml

scale_v1

wgcna

configs/stage9_multiplex_vae.yaml

multiplex_v1

vae

configs/stage9_multiplex_graph.yaml

multiplex_v1

graph

configs/stage9_multiplex_latent.yaml

multiplex_v1

latent

configs/stage9_multiplex_wgcna.yaml

multiplex_v1

wgcna

configs/stress_multiplex_xxlarge_vae.yaml

multiplex_v1

vae

configs/stress_multiplex_xxlarge_wgcna.yaml

multiplex_v1

wgcna

Use them as stable entry points and supply Hydra overrides after -- on the CLI.

Multiplex-Specific Fields

VAE, graph, and latent configs can enable multiplex edge policies with:

  • allow_abundance_abundance — include abundance-abundance edges instead of requiring abundance-only genes to connect through switch-active genes.

  • alpha_switch — threshold for switch-switch feature edges.

  • alpha_abundance — fixed threshold for abundance-abundance feature edges.

  • alpha_abundance_grid — optional grid used to select the smallest abundance threshold that avoids merging baseline switch modules.

For very large multiplex fixtures, prefer a fixed alpha_abundance because grid selection repeats the O(feature²) graph projection for each candidate threshold.

Per-Fixture Overrides

All backends support per-fixture config overrides in BenchmarkCommandConfig:

  • fixture_model_overrides — baseline

  • fixture_latent_overrides — latent

  • fixture_graph_overrides — graph

  • fixture_vae_overrides — vae

  • fixture_wgcna_overrides — wgcna

Values are partial field dicts merged with dataclasses.replace before the fit.