🍑 PEACH Documentation

Contents

🍑 PEACH Documentation#

Version: 0.3.0

Quick Start#

import peach as pc
import scanpy as sc

# Load and prepare data
adata = sc.read_h5ad('your_data.h5ad')
sc.pp.pca(adata, n_comps=50) # recommend using lowest n PCs that makes sense for your dataset for better results

# Train archetypal model
results = pc.tl.train_archetypal(adata, n_archetypes=5, n_epochs=100)

# Extract coordinates and assign cells
pc.tl.archetypal_coordinates(adata)
pc.tl.assign_archetypes(adata, percentage_per_archetype=0.15)

# Statistical analysis
gene_results = pc.tl.gene_associations(adata, fdr_scope='global')

# Visualize
pc.pl.archetypal_space(adata, color_by='archetypes').show()

See Installation for setup instructions.

Tutorials#

Interactive Jupyter notebooks walking through each analysis step:

Tutorial	Description
01_data_loading	Data loading and preprocessing
02_hyperparameter_search	Cross-validation for optimal archetypes
03_model_training	Training archetypal models
04_archetype_coordinates	Coordinate extraction and cell assignment
05_gene_enrichment	Gene and pathway associations
06_cellrank_integration	Trajectory analysis with CellRank
07 pointed to a set of experimental features that are not yet ready for release, check back later
08_visualization	Comprehensive visualization guide

Workflow Scripts#

Standalone Python scripts for each workflow stage:

Script	Purpose
WORKFLOW_01_DATA_LOAD.py	Data loading, QC, PCA
WORKFLOW_02_HYPERPARAM_SEARCH.py	Hyperparameter optimization
WORKFLOW_03_MODEL_TRAINING.py	Model training
WORKFLOW_04_COORDINATES.py	Coordinates and assignment
WORKFLOW_05_ENRICHMENT.py	Gene/pathway enrichment
WORKFLOW_06_CELLRANK.py	CellRank integration
WORKFLOW_08_VISUALIZATION.py	Visualization

API Structure#

pp (5):  load_data, generate_synthetic, prepare_training,
         load_pathway_networks, compute_pathway_scores

tl (16): train_archetypal, hyperparameter_search, archetypal_coordinates,
         assign_archetypes, extract_archetype_weights, gene_associations,
         pathway_associations, pattern_analysis, conditional_associations,
         archetype_exclusive_patterns, specialization_patterns, tradeoff_patterns,
         setup_cellrank, compute_lineage_pseudotimes, compute_lineage_drivers,
         compute_transition_frequencies

pl (14): archetypal_space, archetypal_space_multi, training_metrics,
         elbow_curve, dotplot, archetype_positions, archetype_positions_3d,
         archetype_statistics, pattern_dotplot, pattern_summary_barplot,
         pattern_heatmap, fate_probabilities, gene_trends, lineage_drivers

Developer Reference#

For function signatures and return types, consult (in order):

src/peach/_core/types_index.py (~270 lines) - Function → return type mapping
src/peach/_core/tools_schema.py (~1000 lines) - Function → input parameters
src/peach/_core/types.py - Full Pydantic type definitions (grep as needed)

Additional Resources#