Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.
--- name: arboreto description: Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships. license: MIT author: AIPOCH --- > **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills) ## When to Use - You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges. - You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions. - You need GRN inference that can scale to large datasets using parallel/distributed execution. - You want to compare gradient-boosting–based GRN inference (GRNBoost2) versus random-forest–based inference (GENIE3). - You need a reproducible, scriptable pipeline to generate a ranked network edge list from expression data. ## Key Features - GRN inference from gene expression data using **GRNBoost2** (gradient boosting) or **GENIE3** (random forest). - Scalable execution via **Dask**, from a single machine to multi-node clusters. - Command-line workflow for generating a GRN edge list from a tabular expression matrix. - Algorithm guidance and comparison: see `references/algorithms.md`. - Distributed setup notes: see `references/distributed_computing.md`. ## Dependencies - arboreto - dask - distributed - pandas - scipy - scikit-learn ## Example Usage Run GRN inference from an expression matrix (TSV) and write the inferred network to an output file: ```bash python scripts/infer_network.py \ --input expression_data.tsv \ --output network.tsv \ --algo grnboost2 ``` To use the alternative algorithm: ```bash python scripts/infer_network.py \ --input expression_data.tsv \ --output network.tsv \ --algo genie3 ``` ## Implementation Details - **Input/Output** - **Input**: a gene expression matrix (e.g., TSV) where rows typically represent samples/cells and columns represent genes (exact expectations depend on `scripts/infer_network.py`). - **Output**: a ranked edge list representing inferred regulatory relationships (TF → target) with an importance/weight score. - **Algorithms** - **GRNBoost2**: uses gradient boosting to estimate feature importance of candidate regulators for each target gene; generally preferred for larger datasets due to speed and scalability. - **GENIE3**: uses random forests to compute regulator importance per target gene; a classic baseline for GRN inference. - For a detailed comparison and practical guidance, refer to `references/algorithms.md`. - **Parallel/Distributed Execution** - Computation is parallelized with **Dask**, enabling scaling from local multi-core execution to distributed clusters. - Cluster configuration and deployment considerations are documented in `references/distributed_computing.md`. - **Key Parameters** - `--algo`: selects the inference method (`grnboost2` or `genie3`), affecting runtime and model behavior. - Additional runtime/cluster parameters (if exposed by the script) typically control Dask scheduling, worker counts, and resource usage.--- name: arboreto description: Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships. license: MIT author: AIPOCH --- > **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills) ## When to Use - You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges. - You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions. - You need GRN inference that can scale to large datasets using parallel/distributed execution. - You want to compare gradient-boosting–based GRN inference (GRNBoost2) versus random-forest–based inference (GENIE3). - You need a reproducible, scriptable pipeline to generate a ranked network edge list from expression data. ## Key Features - GRN inference from gene expression data using **GRNBoost2** (gradient boosting) or **GENIE3** (random forest). - Scalable execution via **Dask**, from a single machine to multi-node clusters. - Command-line workflow for generating a GRN edge list from a tabular expression matrix. - Algorithm guidance and comparison: see `references/algorithms.md`. - Distributed setup notes: see `references/distributed_computing.md`. ## Dependencies - arboreto - dask - distributed - pandas - scipy - scikit-learn ## Example Usage Run GRN inference from an expression matrix (TSV) and write the inferred network to an output file: ```bash python scripts/infer_network.py \ --input expression_data.tsv \ --output network.tsv \ --algo grnboost2 ``` To use the alternative algorithm: ```bash python scripts/infer_network.py \ --input expression_data.tsv \ --output network.tsv \ --algo genie3 ``` ## Implementation Details - **Input/Output** - **Input**: a gene expression matrix (e.g., TSV) where rows typically represent samples/cells and columns represent genes (exact expectations depend on `scripts/infer_network.py`). - **Output**: a ranked edge list representing inferred regulatory relationships (TF → target) with an importance/weight score. - **Algorithms** - **GRNBoost2**: uses gradient boosting to estimate feature importance of candidate regulators for each target gene; generally preferred for larger datasets due to speed and scalability. - **GENIE3**: uses random forests to compute regulator importance per target gene; a classic baseline for GRN inference. - For a detailed comparison and practical guidance, refer to `references/algorithms.md`. - **Parallel/Distributed Execution** - Computation is parallelized with **Dask**, enabling scaling from local multi-core execution to distributed clusters. - Cluster configuration and deployment considerations are documented in `references/distributed_computing.md`. - **Key Parameters** - `--algo`: selects the inference method (`grnboost2` or `genie3`), affecting runtime and model behavior. - Additional runtime/cluster parameters (if exposed by the script) typically control Dask scheduling, worker counts, and resource usage.
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Includes source scripts; review them before use. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.