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skills/arboreto/references/basic_inference.md

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+# 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:
+```python
+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:
+```python
+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:
+
+```python
+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
+
+```python
+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
+
+```python
+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:
+
+```python
+network = grnboost2(
+    expression_data=expression_matrix,
+    tf_names=tf_names,
+    seed=777
+)
+```
+
+## Algorithm Selection
+
+Use `grnboost2()` for most cases (faster, handles large datasets):
+```python
+from arboreto.algo import grnboost2
+network = grnboost2(expression_data=expression_matrix)
+```
+
+Use `genie3()` for comparison or specific requirements:
+```python
+from arboreto.algo import genie3
+network = genie3(expression_data=expression_matrix)
+```
+
+See `references/algorithms.md` for detailed algorithm comparison.