Basic GRN Inference with Arboreto

Input Data Requirements

Arboreto requires gene expression data in one of two formats:

Pandas DataFrame (Recommended)

Rows: Observations (cells, samples, conditions)
Columns: Genes (with gene names as column headers)
Format: Numeric expression values

Example:

import pandas as pd

# Load expression matrix with genes as columns
expression_matrix = pd.read_csv('expression_data.tsv', sep='\t')
# Columns: ['gene1', 'gene2', 'gene3', ...]
# Rows: observation data

NumPy Array

Shape: (observations, genes)
Requirement: Separately provide gene names list matching column order

Example:

import numpy as np

expression_matrix = np.genfromtxt('expression_data.tsv', delimiter='\t', skip_header=1)
with open('expression_data.tsv') as f:
    gene_names = [gene.strip() for gene in f.readline().split('\t')]

assert expression_matrix.shape[1] == len(gene_names)

Transcription Factors (TFs)

Optionally provide a list of transcription factor names to restrict regulatory inference:

from arboreto.utils import load_tf_names

# Load from file (one TF per line)
tf_names = load_tf_names('transcription_factors.txt')

# Or define directly
tf_names = ['TF1', 'TF2', 'TF3']

If not provided, all genes are considered potential regulators.

Basic Inference Workflow

Using Pandas DataFrame

import pandas as pd
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # Load expression data
    expression_matrix = pd.read_csv('expression_data.tsv', sep='\t')

    # Load transcription factors (optional)
    tf_names = load_tf_names('tf_list.txt')

    # Run GRN inference
    network = grnboost2(
        expression_data=expression_matrix,
        tf_names=tf_names  # Optional
    )

    # Save results
    network.to_csv('network_output.tsv', sep='\t', index=False, header=False)

Critical: The if __name__ == '__main__': guard is required because Dask spawns new processes internally.

Using NumPy Array

import numpy as np
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # Load expression matrix
    expression_matrix = np.genfromtxt('expression_data.tsv', delimiter='\t', skip_header=1)

    # Extract gene names from header
    with open('expression_data.tsv') as f:
        gene_names = [gene.strip() for gene in f.readline().split('\t')]

    # Verify dimensions match
    assert expression_matrix.shape[1] == len(gene_names)

    # Run inference with explicit gene names
    network = grnboost2(
        expression_data=expression_matrix,
        gene_names=gene_names,
        tf_names=tf_names
    )

    network.to_csv('network_output.tsv', sep='\t', index=False, header=False)

Output Format

Arboreto returns a Pandas DataFrame with three columns:

Column	Description
`TF`	Transcription factor (regulator) gene name
`target`	Target gene name
`importance`	Regulatory importance score (higher = stronger regulation)

Example output:

TF1    gene5    0.856
TF2    gene12   0.743
TF1    gene8    0.621

Setting Random Seed

For reproducible results, provide a seed parameter:

network = grnboost2(
    expression_data=expression_matrix,
    tf_names=tf_names,
    seed=777
)

Algorithm Selection

Use grnboost2() for most cases (faster, handles large datasets):

from arboreto.algo import grnboost2
network = grnboost2(expression_data=expression_matrix)

Use genie3() for comparison or specific requirements:

from arboreto.algo import genie3
network = genie3(expression_data=expression_matrix)

See references/algorithms.md for detailed algorithm comparison.

3.7 KiB Raw Blame History