arboreto · diff
v1.2 to v1.3
134 added, 220 removed. Audit A to A.
---
name: arboreto
- description: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
+ description: Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
license: BSD-3-Clause license
+ compatibility: Requires the isolated Python 3.11 compatibility stack below, including Arboreto, Dask/distributed, NumPy, pandas, scikit-learn and SciPy. Network access is needed for installation, not local inference. No credentials required.
metadata:
- version: "1.2"
+ version: "1.3"
skill-author: K-Dense Inc.
+ last-reviewed: "2026-09-30"
---
# Arboreto
- ## Overview
-
- Arboreto is a Python library from [Aerts Lab](https://github.com/aertslab/arboreto) for inferring gene regulatory networks (GRNs) from gene expression data. It parallelizes tree-based ensemble regression (GRNBoost2, GENIE3) with [Dask](https://distributed.dask.org/) across local cores or remote clusters.
-
- **Core capability**: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions).
-
- **Upstream**: PyPI **0.1.6** (2021-02-09, latest). Docs: [arboreto.readthedocs.io](https://arboreto.readthedocs.io/en/latest/). Primary downstream consumer: [pySCENIC](https://github.com/aertslab/pySCENIC).
-
- ## Quick Start
-
- Install arboreto:
- ```bash
- uv pip install arboreto
- ```
-
- Basic GRN inference:
- ```python
- import pandas as pd
- from arboreto.algo import grnboost2
-
- if __name__ == '__main__':
- # Load expression data (genes as columns)
- expression_matrix = pd.read_csv('expression_data.tsv', sep='\t')
-
- # Infer regulatory network
- network = grnboost2(expression_data=expression_matrix)
-
- # Save results (TF, target, importance)
- network.to_csv('network.tsv', sep='\t', index=False, header=False)
- ```
-
- **Critical**: Always use `if __name__ == '__main__':` guard because Dask spawns new processes.
+ ## When to use
- ## Core Capabilities
+ Use Arboreto to rank candidate regulator-target associations from expression
+ measurements. GRNBoost2 fits stochastic gradient boosting regressions; GENIE3
+ fits random forests. Each target is predicted from candidate regulators, excluding
+ itself. These are observational predictive associations, not proof of direct
+ binding, activation/repression, or causal regulation.
- ### 1. Basic GRN Inference
+ The latest PyPI release checked is **0.1.6** (2021-02-09). Read the Docs still
+ labels its documentation 0.1.5; the current GitHub source contains fixes that are
+ **not in the PyPI wheel**. Do not assume a successful unpinned installation can
+ run inference. See [compatibility and distribution details](references/distributed_computing.md).
- For standard GRN inference workflows including:
- - Input data preparation (Pandas DataFrame or NumPy array)
- - Running inference with GRNBoost2 or GENIE3
- - Filtering by transcription factors
- - Output format and interpretation
+ ## Installation and compatibility
- **See**: `references/basic_inference.md`
+ The following exact stack passed dense and CSC-sparse GRNBoost2, dense GENIE3,
+ custom GBM/RF, and wrapper smoke tests on macOS arm64 with Python 3.11.11:
- **Use the ready-to-run script**: `scripts/basic_grn_inference.py` for standard inference tasks:
```bash
- python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777 --limit 5000
+ uv venv --python 3.11 .venv-arboreto
+ uv pip install --python .venv-arboreto/bin/python \
+ 'arboreto==0.1.6' 'dask[complete]==2024.7.1' 'distributed==2024.7.1' \
+ 'numpy==1.26.4' 'pandas==2.2.3' 'scikit-learn==1.5.2' 'scipy==1.13.1'
```
- ### 2. Algorithm Selection
-
- Arboreto provides two algorithms:
-
- **GRNBoost2 (Recommended)**:
- - Fast gradient boosting-based inference
- - Optimized for large datasets (10k+ observations)
- - Default choice for most analyses
-
- **GENIE3**:
- - Random Forest-based inference
- - Original multiple regression approach
- - Use for comparison or validation
+ This is a bounded compatibility recipe, not a claim that current releases of all
+ dependencies work. PyPI Arboreto builds an empty metadata graph that the newer
+ Dask dataframe implementation rejects. For this pinned Dask version, select its
+ legacy dataframe backend **before importing Arboreto or `dask.dataframe`**:
- Quick comparison:
```python
+ import dask
+ dask.config.set({"dataframe.query-planning": False})
from arboreto.algo import grnboost2, genie3
-
- # Fast, recommended
- network_grnboost = grnboost2(expression_data=matrix)
-
- # Classic algorithm
- network_genie3 = genie3(expression_data=matrix)
```
- **For detailed algorithm comparison, parameters, and selection guidance**: `references/algorithms.md`
-
- ### 3. Distributed Computing
-
- Scale inference from local multi-core to cluster environments:
-
- **Local (default)** - Uses all available cores automatically:
- ```python
- network = grnboost2(expression_data=matrix)
- ```
-
- **Custom local client** - Control resources:
- ```python
- from distributed import LocalCluster, Client
-
- local_cluster = LocalCluster(n_workers=10, memory_limit='8GB')
- client = Client(local_cluster)
-
- network = grnboost2(expression_data=matrix, client_or_address=client)
-
- client.close()
- local_cluster.close()
- ```
+ The bundled wrapper does this for Dask 2024.7.1. Restart an existing notebook
+ kernel if it has already imported the newer dataframe backend. Sparse targets
+ also use `.A` inside Arboreto 0.1.6; this attribute was removed in SciPy 1.14.
+ Keep the tested SciPy pin for sparse inference. No monkeypatch to site-packages
+ is required by this recipe.
- **Cluster computing** - Connect to remote Dask scheduler:
- ```python
- from distributed import Client
+ ## Workflow
- client = Client('tcp://scheduler:8786')
- network = grnboost2(expression_data=matrix, client_or_address=client)
- ```
+ 1. Select biologically comparable cells/samples; document normalization, filtering,
+ batch handling, organism, identifier namespace, and expression layer.
+ 2. Prepare **rows = observations, columns = genes**. Exclude sample IDs from
+ expression values. Require unique gene names, numeric finite values, and a TF
+ list with a nonempty overlap. All-zero/constant genes provide no useful targets.
+ 3. Choose GRNBoost2 for an efficient starting analysis, GENIE3 for method comparison,
+ or `diy` for explicit regressor settings. See [algorithms](references/algorithms.md).
+ 4. Run a small subset first in the pinned environment, then scale worker counts to
+ available memory. Keep the `if __name__ == "__main__":` guard in process-based scripts.
+ 5. Inspect worker warnings and target coverage, save the full ranked network, and
+ assess stability across seeds and resampled observations before prioritizing edges.
- **For cluster setup, performance optimization, and large-scale workflows**: `references/distributed_computing.md`
+ ## Run the bundled wrapper
- ## Installation
+ From this skill directory, with a TSV containing gene headers and numeric rows:
```bash
- uv pip install arboreto
+ .venv-arboreto/bin/python scripts/basic_grn_inference.py expression_data.tsv network.tsv \
+ --tf-file tfs.txt --seed 777 --workers 2 --limit 5000
```
- Conda (Bioconda):
-
- ```bash
- conda install -c bioconda arboreto
- ```
+ Add `--index-col 0` only if the first column contains cell/sample identifiers.
+ The wrapper rejects duplicate headers before pandas can rename them, nonnumeric
+ or nonfinite values, empty TF overlap, invalid limits, and wholly empty results.
+ It reports TF overlap and uses a fresh bounded Dask client that closes on error.
+ The default is one worker; increase it after a successful pilot. Without a TF
+ file, **all genes** are candidate regulators, even though the output column is
+ named `TF`.
- **Dependencies** (from upstream `requirements.txt`): `dask[complete]`, `distributed`, `numpy`, `pandas`, `scikit-learn`, `scipy`
+ Output is a headerless TSV in `TF`, `target`, `importance` order. For downstream
+ consumers that require column headers (including pySCENIC adjacency loading),
+ write a separate copy with `header=True` rather than assuming every tool accepts
+ the headerless upstream example format.
- **Input formats**: pandas DataFrame, dense `numpy.ndarray`, or sparse `scipy.sparse.csc_matrix` (rows = observations, columns = genes). For array/matrix inputs, pass `gene_names` explicitly.
+ ## Minimal Python example
- ## Common Use Cases
+ This synthetic example checks execution and output structure; it is not a
+ biological benchmark. The same calls were tested with a 32-observation,
+ four-gene fixture.
- ### Single-Cell RNA-seq Analysis
```python
+ import dask
+ dask.config.set({"dataframe.query-planning": False})
+ import numpy as np
import pandas as pd
from arboreto.algo import grnboost2
-
- if __name__ == '__main__':
- # Load single-cell expression matrix (cells x genes)
- sc_data = pd.read_csv('scrna_counts.tsv', sep='\t')
-
- # Infer cell-type-specific regulatory network
- network = grnboost2(expression_data=sc_data, seed=42)
-
- # Filter high-confidence links
- high_confidence = network[network['importance'] > 0.5]
- high_confidence.to_csv('grn_high_confidence.tsv', sep='\t', index=False)
- ```
-
- ### Bulk RNA-seq with TF Filtering
- ```python
- from arboreto.utils import load_tf_names
- from arboreto.algo import grnboost2
-
- if __name__ == '__main__':
- # Load data
- expression_data = pd.read_csv('rnaseq_tpm.tsv', sep='\t')
- tf_names = load_tf_names('human_tfs.txt')
-
- # Infer with TF restriction
- network = grnboost2(
- expression_data=expression_data,
- tf_names=tf_names,
- seed=123
- )
-
- network.to_csv('tf_target_network.tsv', sep='\t', index=False)
- ```
-
- ### Comparative Analysis (Multiple Conditions)
- ```python
- from arboreto.algo import grnboost2
-
- if __name__ == '__main__':
- # Infer networks for different conditions
- conditions = ['control', 'treatment_24h', 'treatment_48h']
-
- for condition in conditions:
- data = pd.read_csv(f'{condition}_expression.tsv', sep='\t')
- network = grnboost2(expression_data=data, seed=42)
- network.to_csv(f'{condition}_network.tsv', sep='\t', index=False)
- ```
-
- ## Output Interpretation
-
- Arboreto returns a DataFrame with regulatory links:
-
- | Column | Description |
- |--------|-------------|
- | `TF` | Transcription factor (regulator) |
- | `target` | Target gene |
- | `importance` | Regulatory importance score (higher = stronger) |
-
- `importance` ranks predictive contributions in the fitted regressions; it is not
- a binding probability, p-value, or proof of direct causal regulation. The example
- cutoff of 0.5 is illustrative, not a calibrated high-confidence threshold. Compare
- matched preprocessing, sample sizes, gene sets, and seed stability before calling
- a link condition-specific; validate candidate edges with motif or perturbation evidence.
-
- **Filtering strategy**:
- - `limit=N` at inference time (return top N links globally)
- - Post-hoc importance threshold (e.g., > 0.5)
- - Top links per target via `groupby('target')`
- - Statistical significance testing (permutation tests, external tools)
-
- ## Integration with pySCENIC
-
- Arboreto powers the GRN inference step in [pySCENIC](https://github.com/aertslab/pySCENIC). pySCENIC 0.11+ passes sparse expression matrices to `grnboost2` / `genie3`; pySCENIC 0.12+ defaults to `arboreto_with_multiprocessing.py` (no Dask) for compatibility — use standalone arboreto when you need Dask scaling.
-
- ```python
- # Standalone: infer co-expression modules before pySCENIC cisTarget pruning
- from arboreto.algo import grnboost2
-
- network = grnboost2(expression_data=expression_df, tf_names=tf_list, limit=5000)
+ from distributed import Client, LocalCluster
- # Downstream: pySCENIC ctx pruning, regulon definition, AUCell (see pySCENIC docs)
+ if __name__ == "__main__":
+ rng = np.random.default_rng(123)
+ values = rng.normal(size=(32, 4))
+ values[:, 2] = 3 * values[:, 0] + rng.normal(scale=0.1, size=32)
+ matrix = pd.DataFrame(values, columns=["TF1", "TF2", "G1", "G2"])
+ with LocalCluster(n_workers=1, threads_per_worker=1,
+ dashboard_address=None) as cluster, Client(cluster) as client:
+ network = grnboost2(expression_data=matrix, tf_names=["TF1", "TF2"],
+ seed=777, client_or_address=client)
+ assert not network.empty
+ assert not (network["TF"] == network["target"]).any()
+ network.to_csv("network.tsv", sep="\t", index=False, header=False)
```
- Convert AnnData to a DataFrame for arboreto directly:
-
- ```python
- expression_df = adata.to_df() # cells x genes
- ```
+ For real DataFrame, ndarray, CSC, and AnnData input conventions, read
+ [basic inference](references/basic_inference.md).
- ## Reproducibility
+ ## Interpret and validate output
- Always set a seed for reproducible results:
- ```python
- network = grnboost2(expression_data=matrix, seed=777)
- ```
+ | Column | Meaning |
+ | --- | --- |
+ | `TF` | Candidate predictor gene, restricted only if a TF list was supplied |
+ | `target` | Gene whose expression was predicted |
+ | `importance` | Nonnegative feature importance used to rank candidate links |
- Run multiple seeds for robustness analysis:
- ```python
- from distributed import LocalCluster, Client
+ Results are sorted by decreasing importance; zero-importance links are omitted.
+ GRNBoost2 rescales feature importance by the fitted number of trees, so its
+ scores can exceed 1 and are not on the same scale as GENIE3. There is no universal
+ `importance > 0.5` confidence cutoff. `limit=N` keeps the top N links globally;
+ it does not limit target regressions or return N links per target.
- if __name__ == '__main__':
- client = Client(LocalCluster())
+ For consensus, define a per-run selection rule first, then count the fraction of
+ **all runs** retaining each TF-target pair. An edge missing from a run is not an
+ observed score to average only over present rows. Archive individual networks,
+ seeds, package versions, filters, and identifier lists. Match preprocessing,
+ sample sizes and gene sets across conditions; differences in scores alone do not
+ establish differential regulation. Use independent motif, binding or perturbation
+ evidence to assess candidates. Agreement between GRNBoost2 and GENIE3 is method
+ sensitivity analysis, not independent biological validation.
- seeds = [42, 123, 777]
- networks = []
+ Upstream retries target-level regression failures and can return empty target
+ results after warnings. A nonempty overall network does not prove every target
+ fit succeeded. Check logs and expected target coverage; absence of an edge may
+ reflect zero importance, filtering, missing predictors, or a failed regression.
- for seed in seeds:
- net = grnboost2(expression_data=matrix, client_or_address=client, seed=seed)
- networks.append(net)
+ ## pySCENIC boundary
- # Consensus: links recurring across runs (example: mean importance per TF-target pair)
- import pandas as pd
- combined = pd.concat(networks)
- consensus = (
- combined.groupby(['TF', 'target'], as_index=False)['importance']
- .mean()
- .query('importance > 0.5')
- )
- ```
+ Arboreto supplies the adjacency inference stage; motif pruning/regulon definition
+ and AUCell are separate downstream steps. pySCENIC supplies the separate
+ `arboreto_with_multiprocessing.py` utility to run inference without Dask. Do not
+ assume `pyscenic grn` automatically uses that utility: the reviewed CLI still
+ calls Arboreto with a Dask client. Its `custom_multiprocessing` default concerns
+ `ctx` pruning. Downstream pySCENIC execution was not tested in this refresh.
## Troubleshooting
- **Memory errors**: Reduce dataset size by filtering low-variance genes or use distributed computing
-
- **Slow performance**: Use GRNBoost2 instead of GENIE3, enable distributed client, filter TF list
-
- **Dask errors**: Ensure `if __name__ == '__main__':` guard is present in scripts (required on Windows/macOS with spawn-based multiprocessing)
+ - **`Must supply at least one delayed object`**: check the installed release and
+ Dask backend first; this can be PyPI 0.1.6's empty metadata graph even with valid input.
+ - **Sparse `.A` error or repeated empty targets**: use the tested SciPy pin and
+ `scipy.sparse.csc_matrix`, not a newer sparse array type.
+ - **Import error with very old Dask**: Dask 2023.12.1 failed on Python 3.11.11's
+ `inspect` behavior during review; do not mix arbitrary old and new components.
+ - **Cancelled futures on repeat runs**: use a fresh client/cluster per run when
+ reusing scattered inputs triggers this error; a repeated in-process client
+ probe hit it during review, while separate process clients passed.
+ - **Memory pressure**: reduce worker count, restrict regulators, and estimate
+ dense matrix plus per-worker TF copies before scaling. A cluster does not make
+ the client-side expression matrix out-of-core.
- **Empty results**: Check data format (genes as columns), verify TF names match column names in the expression matrix
+ ## Sources and review scope
- **Sparse data**: Use `scipy.sparse.csc_matrix` and pass matching `gene_names`; supported since arboreto 0.1.6 / pySCENIC 0.11
+ Reviewed 2026-09-30: [PyPI release](https://pypi.org/project/arboreto/0.1.6/),
+ [official guide](https://arboreto.readthedocs.io/en/latest/userguide.html),
+ [algorithm source](https://github.com/aertslab/arboreto/blob/master/arboreto/algo.py),
+ [core source](https://github.com/aertslab/arboreto/blob/master/arboreto/core.py),
+ [Dask 2024.7.1 backend selection](https://github.com/dask/dask/blob/2024.7.1/dask/dataframe/__init__.py),
+ [SciPy 1.14 removals](https://docs.scipy.org/doc/scipy/release/1.14.0-notes.html), and
+ [pySCENIC CLI](https://github.com/aertslab/pySCENIC/blob/master/src/pyscenic/cli/pyscenic.py).
+ Local synthetic runs verify mechanics only. Remote scheduling, large biological
+ datasets, Windows/Linux, and pySCENIC downstream analysis remain untested.
+ There are no hosted service endpoints, authentication, or pagination in this skill.
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