ot-grn 0.1.4

Double Optimal Transport for Differential Gene Regulatory Network Inference with Unpaired Samples

ot-grn

Double Optimal Transport for Differential Gene Regulatory Network Inference with Unpaired Samples

ot-grn is a Python library designed to infer differential gene regulatory networks (GRNs) from gene expression data using optimal transport (OT) theory, applicable to unpaired samples. The library implements a robust and scalable framework, named Double OT, that integrates OT at the gene and sample levels to reconstruct GRNs effectively.

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Features

• Data Preparaion: Generate simulated datasets; load real-world gastric cancer gene expression data.
• Network Inference: Use the Double OT method to align unpaired samples and infer regulatory relationships.
• Evaluation and Interpretation: Evaluate the inferred network using metrics like AUROC, AUPR, and early precision; extract key regulatory edges and genes from the inferred network.

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Installation

You can install the library via pip:

pip install ot-grn

To install from source:

git clone https://github.com/Mengyu8042/ot-grn.git
cd ot-grn
pip install .

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Quick Start

1. Simulation Studies

from ot_grn import generate_simulated_data, double_ot, evaluate_grn_accuracy

# (i) Generate synthetic normal and tumor expression data
exp1, exp2, true_plan = generate_simulated_data(p=500, n=100, diffgene=0.2, indegree=5)

# (ii) Infer differential GRN using Double OT method
ot_plan = double_ot(exp1, exp2)

# (iii) Calculate evaluation metrics for the inferred GRN
auroc, aupr, ep = evaluate_grn_accuracy(true_plan, ot_plan)
print(f"AUROC: {auroc}, AUPR: {aupr}, Early Precision: {ep}")

2. Real Data Analyses

from ot_grn import load_gastric_cancer_data, double_ot, extract_top_edges

# (i) Load normal and tumor expression data from gastric cancer patients
exp1, exp2 = load_gastric_cancer_data(same_source=True, paired=True)

# (ii) Infer differential GRN using Double OT method
ot_plan = double_ot(exp1, exp2)

# (iii) Extract top regulatory edges and key genes
gene_names = exp1.index.tolist()
top_edges, key_genes = extract_top_edges(ot_plan, gene_names, num=500)
print(top_edges)
print(key_genes)

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Documentation

Core Functions

1. generate_simulated_data

   • Generates simulated datasets for testing GRN inference methods.
   • Parameters:
     • p: Number of genes.
     • n: Number of samples.
     • diffgene (optional): Proportion of differentially expressed genes, by default 0.2.
     • indegree (optional): Expected number of parents for differential genes, by default 5.
     • snr (optional): Signal-to-noise ratio, by default 2.
     • outlier_ratio (optional): Proportion of outliers, by default 0.
   • Returns: Normal expression matrix, tumor expression matrix, and true regulatory relationships.

2. load_gastric_cancer_data

   • Loads gastric cancer gene expression data from the same or different sources.
   • Parameters:
     • same_source (optional): If True, normal and tumor samples are from the same source, by default True.
     • paired (optional): Only relevant if same_source is True. If True, samples are paired, by default True.
   • Returns: Normal and tumor expression matrices.

3. double_ot

   • Implements the Double OT method for differential GRN inference.
   • Parameters:
     • exp1: Expression matrix for condition 1 (e.g., normal state).
     • exp2: Expression matrix for condition 2 (e.g., tumor state).
     • paired (optional): If True, assumes the samples are paired. If False, uses partial OT to align samples, by default True.
     • reg_m (optional): Marginal relaxation hyperparameter, by default 0.05.
     • reg (optional): Entropy regularization hyperparameter, by default 0.05.
     • n_components (optional): Number of principal components for PCA in sample alignment, by default None (all components).
     • return_alignment (optional): If True and samples are unpaired (paired=False), returns the sample alignment result (sample-level OT plan), by default False.
   • Returns: Gene-level OT plan (and sample-level OT plan).

4. evaluate_grn_accuracy

   • Calculate performance metrics for the inferred GRN.
   • Parameters:
     • y_true: Ground truth binary labels (genes x genes).
     • y_score: Predicted scores (genes x genes).
   • Returns: AUROC, AUPR, Early Precision.

5. extract_top_edges

   • Extracts top regulatory edges and associated genes from the inferred GRN.
   • Parameters:
     • y_score: Predicted scores (genes x genes).
     • gene_names: List of gene names.
     • num: Number of top edges to extract.
   • Returns: DataFrame of top edges and a list of connected genes.

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Dependencies

• Python 3.7 or higher
• numpy
• scipy
• pandas
• scikit-learn
• pot (Python Optimal Transport library)

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

For questions or issues, please contact Mengyu Li at [e-mail] or open an issue on the GitHub repository.