pythonflex 0.3.4

pythonFLEX is a benchmarking toolkit for evaluating CRISPR screen results against biological gold standards. The toolkit computes gene-level and complex-level performance metrics, helping researchers systematically assess the biological relevance and resolution of their CRISPR screening data.

pythonFLEX

🧬 pythonFLEX is a benchmarking toolkit for evaluating CRISPR screen results against biological gold standards. It provides precision-recall analysis using reference gene sets from CORUM protein complexes, Gene Ontology Biological Processes (GO-BP), KEGG pathways, and other curated resources. The toolkit computes gene-level and complex-level performance metrics, helping researchers systematically assess the biological relevance and resolution of their CRISPR screening data.

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🔧 Features

• Precision-recall curve generation for ranked gene lists

• Evaluation using CORUM complexes, GO terms, pathways

• Complex-level resolution analysis and visualization

• Easy integration into CRISPR screen workflows

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📦 Installation

Suggested to use Python version 3.10 with virtual env.

Create venv

conda create -n p310 python=3.10
conda activate p310
pip install uv

Install pythonFLEX via pip

uv pip install pythonflex

or

pip install pythonflex

or Install pythonFLEX via git (to develop package in local)

git clone https://github.com/tyasird/pythonFLEX.git
cd pythonFLEX
uv pip install -e .

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🚀 Quickstart

import pythonflex as flex

inputs = {
    "Melanoma (63 Screens)": {
        "path": flex.get_example_data_path("melanoma_cell_lines_500_genes.csv"), 
        "sort": "high"
    },
    "Liver (24 Screens)": {
        "path": flex.get_example_data_path("liver_cell_lines_500_genes.csv"), 
        "sort": "high"
    },
    "Neuroblastoma (37 Screens)": {
        "path": flex.get_example_data_path("neuroblastoma_cell_lines_500_genes.csv"), 
        "sort": "high"
    },
}



default_config = {
    "min_genes_in_complex": 2,
    "min_genes_per_complex_analysis": 2,
    "output_folder": "output",
    "gold_standard": "GOBP",
    "color_map": "RdYlBu",
    "jaccard": True,
    "jaccard_threshold": 1.0,  # set e.g. 0.90 to remove highly similar terms
    "plotting": {
        "save_plot": True,
        "output_type": "png",
    },
    "preprocessing": {
        "fill_na": True,
        "normalize": False,
    },
    "corr_function": "numpy",
    "logging": {  
        "visible_levels": ["DONE","INFO", "WARNING"]  # "PROGRESS", "STARTED", ,"INFO","WARNING"
    }
}


# Initialize logger, config, and output folder
flex.initialize(default_config)

# Load datasets and gold standard terms
data, _ = flex.load_datasets(inputs)
terms, genes_in_terms = flex.load_gold_standard()

# Run analysis
for name, dataset in data.items():
    df, pr_auc = flex.pra(name, dataset)
    fpc = flex.pra_percomplex(name, dataset, is_corr=False) 
    cc = flex.complex_contributions(name)

# Generate plots
flex.plot_auc_scores()
flex.plot_precision_recall_curve()
flex.plot_percomplex_scatter()
flex.plot_percomplex_scatter_bysize()
flex.plot_significant_complexes()
flex.plot_complex_contributions()
flex.plot_mpr_tp_multi(show_filters="all")
flex.plot_mpr_complexes_multi(show_filters="all")
flex.plot_mpr_complexes_auc_scores("all")

flex.save_results_to_csv()

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📂 Examples

• src/pythonflex/examples/basic_usage.py

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📃 License

MIT