pflex 1.1

pflex is a benchmarking toolkit for evaluating CRISPR screen results against biological functional 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.

pFLEX

Abstract

Genetic networks derived from omics data are a powerful tool for systematic gene function prediction. Performance evaluation of such predictions is crucial to judge the data and computational pipeline to derive the networks, but functional diversity within protein complex or pathway standards often cause hidden evaluation biases. To visualize and mitigate such biases, we recently developed an R package FLEX. Here, we present the FLEX genetic network benchmarking tool as Python library with new and improved functionality. The pFLEX library improves the overall runtime 4.1 to 15.8-fold. It offers additional evaluation metrics that allow for an easy comparison of precision recall performance at the complex or pathway resolution between genetic networks. We demonstrate the utility of pFLEX for evaluating tissue-specific co-essentiality networks and data normalization strategies of the Cancer Dependency Map. This illustrates how different biological module-resolved precision recall metrics in pFLEX enable sensitive and fast evaluation of genetic networks.

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Features

• Precision-recall curve generation for ranked gene lists
• Evaluation using CORUM complexes, GO terms, and pathways
• Complex-level resolution analysis and visualization
• Easy integration into CRISPR screen workflows
• Packaged DepMap example inputs filtered to CORUM genes

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Installation

pFLEX is developed and tested with Python 3.10. We recommend installing it in a dedicated Python 3.10 environment to keep the package and its scientific Python dependencies separate from other projects.

Create venv:

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

Install pFLEX via pip:

uv pip install pflex

or:

pip install pflex

or install pFLEX via git to develop the package locally:

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

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Usage

Full documentation is available at https://tyasird.github.io/pFLEX/.

Input Data

pFLEX expects each input dataset as a matrix with genes in rows and screens, samples, or cell lines in columns.

Gene	ACH-000014	ACH-000219	ACH-000274
A2M	-0.125	-0.215	0.065
AATF	0.042	-0.088	-0.016
BCL6	-0.019	0.112	-0.074

CSV, Excel, Parquet, and .p files are supported; .p files are read as Parquet. Parquet is recommended for larger matrices.

The packaged example inputs are real DepMap 25Q2 tissue subsets filtered to genes present in CORUM:

• skin_cell_lines_corum_genes.parquet: 3,465 genes x 75 cell lines
• soft_tissue_cell_lines_corum_genes.parquet: 3,465 genes x 46 cell lines

Use flex.example_input_path() to resolve packaged example inputs:

import pflex as flex

inputs = {
    "Skin": {
        "path": flex.example_input_path("skin_cell_lines_corum_genes.parquet"),
        "sort": "high",
        "color": "#4E79A7",
    },
    "Soft Tissue": {
        "path": flex.example_input_path("soft_tissue_cell_lines_corum_genes.parquet"),
        "sort": "high",
        "color": "#F28E2B",
    },
}

Configuration

config = {
    "functional_standard": "CORUM",
    "min_genes_in_complex": 2,
    "min_genes_per_complex_analysis": 2,
    "output_folder": "output",
    "analysis_genes": "shared",
    "jaccard": True,
    "preprocessing": {
        "fill_na": True,
    },
    "corr_function": "numpy_without_mask",
    "per_complex": {
        "n_jobs": 8,
    },
    "plotting": {
        "save_plot": True,
        "output_type": "png",
    },
}

Common choices:

• functional_standard: "CORUM", "GOBP", "PATHWAY", or a custom .csv path
• analysis_genes: "shared" or "dataset_specific"
• sort: "high" or "low" per input dataset
• preprocessing.fill_na: fill missing values with gene means
• corr_function: "numpy", "numpy_without_mask", "numba", or "pandas"
• per_complex.n_jobs: worker count for per-complex analysis

Analysis Flow

flex.initialize(config)
data, common_genes = flex.load_datasets(inputs)
terms, _ = flex.load_functional_standard()

for name, dataset in data.items():
    corr = flex.perform_corr(dataset, config["corr_function"])
    flex.pra(name, corr, is_corr=True)
    flex.pra_percomplex(name, corr, is_corr=True)
    flex.complex_contributions(name)
    flex.mpr_prepare(name)

flex.plot_precision_recall_curve()
flex.plot_auc_scores()
flex.plot_significant_complexes()
flex.plot_percomplex_scatter(n_top=10)
flex.plot_percomplex_scatter_bysize(n_top=10)
flex.plot_complex_contributions()
flex.plot_mpr_summary()
flex.save_results_to_csv()

See the User Guide for a detailed explanation of every input field, configuration key, function, return value, and output.

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Quickstart

import pflex as flex

inputs = {
    "Skin": {
        "path": flex.example_input_path("skin_cell_lines_corum_genes.parquet"),
        "sort": "high",
        "color": "#4E79A7",
    },
    "Soft Tissue": {
        "path": flex.example_input_path("soft_tissue_cell_lines_corum_genes.parquet"),
        "sort": "high",
        "color": "#F28E2B",
    },
}

config = {
    "functional_standard": "CORUM",
    "output_folder": "output",
    "analysis_genes": "shared",
    "jaccard": True,
    "preprocessing": {
        "fill_na": True,
    },
    "corr_function": "numpy_without_mask",
}

flex.initialize(config)
data, _ = flex.load_datasets(inputs)

for name, dataset in data.items():
    corr = flex.perform_corr(dataset, config["corr_function"])
    flex.pra(name, corr, is_corr=True)

flex.plot_precision_recall_curve()
flex.plot_auc_scores()

For a runnable full workflow, see src/pflex/examples/basic_usage.py.

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License

MIT