GRN Inference Algorithms

Arboreto provides two high-level algorithms for gene regulatory network (GRN) inference, both based on the multiple regression approach.

Algorithm Overview

Both algorithms follow the same inference strategy: 1. For each target gene in the dataset, train a regression model 2. Identify the most important features (potential regulators) from the model 3. Emit these features as candidate regulators with importance scores

The key difference is computational efficiency and the underlying regression method.

GRNBoost2 (Recommended)

Purpose: Fast GRN inference for large-scale datasets using gradient boosting.

When to Use

• Large datasets: Tens of thousands of observations (e.g., single-cell RNA-seq)
• Time-constrained analysis: Need faster results than GENIE3
• Default choice: GRNBoost2 is the flagship algorithm and recommended for most use cases

Technical Details

• Method: Stochastic gradient boosting with early-stopping regularization
• Performance: Significantly faster than GENIE3 on large datasets
• Output: Same format as GENIE3 (TF-target-importance triplets)

Usage

from arboreto.algo import grnboost2

network = grnboost2(
    expression_data=expression_matrix,
    tf_names=tf_names,
    seed=42,
    limit=5000,
)

Parameters (grnboost2)

grnboost2(
    expression_data,              # DataFrame, ndarray, or scipy.sparse.csc_matrix
    gene_names=None,              # Required for ndarray/sparse inputs
    tf_names='all',                 # TF list, None/'all' → all genes as regulators
    client_or_address='local',      # 'local', scheduler address, or Dask Client
    early_stop_window_length=25,    # Early-stopping window (GRNBoost2 only)
    limit=None,                     # Return top N links globally
    seed=None,                      # Random seed; None = non-deterministic
    verbose=False,
)

GENIE3

Purpose: Classic Random Forest-based GRN inference, serving as the conceptual blueprint.

When to Use

• Smaller datasets: When dataset size allows for longer computation
• Comparison studies: When comparing with published GENIE3 results
• Validation: To validate GRNBoost2 results

Technical Details

• Method: Random Forest regression (ExtraTrees available via diy)
• Foundation: Original multiple regression GRN inference strategy
• Trade-off: More computationally expensive but well-established

Usage

from arboreto.algo import genie3

network = genie3(
    expression_data=expression_matrix,
    tf_names=tf_names,
    seed=42,
)

Parameters (genie3)

genie3(
    expression_data,
    gene_names=None,
    tf_names='all',
    client_or_address='local',
    limit=None,
    seed=None,
    verbose=False,
)

Algorithm Comparison

Advanced: Custom Regressors with diy

For custom scikit-learn regressor settings, use diy() (not grnboost2/genie3 kwargs):

from arboreto.algo import diy
from arboreto.core import SGBM_KWARGS, RF_KWARGS

# Custom GRNBoost2-style run
custom_gbm = diy(
    expression_data=expression_matrix,
    regressor_type='GBM',  # 'RF', 'GBM', or 'ET'
    regressor_kwargs={
        **SGBM_KWARGS,
        'n_estimators': 100,
        'max_depth': 5,
        'learning_rate': 0.1,
    },
    tf_names=tf_names,
    seed=42,
)

# Custom GENIE3-style run
custom_rf = diy(
    expression_data=expression_matrix,
    regressor_type='RF',
    regressor_kwargs={
        **RF_KWARGS,
        'n_estimators': 1000,
        'max_features': 'sqrt',
    },
    tf_names=tf_names,
)

Import default kwargs from arboreto.core and override only the keys you need.

Choosing the Right Algorithm

Decision guide:

1. Start with GRNBoost2 — faster and better suited to large single-cell datasets
2. Use GENIE3 if:
3. Comparing with existing GENIE3 publications
4. Dataset is small-medium sized
5. Validating GRNBoost2 results
6. Use diy() if you need non-default regressor hyperparameters

Both algorithms produce comparable regulatory networks with the same output format.