DiNetxify 0.3.2

About DiNetxify

DiNetxify is an open-source Python package for three-dimensional (3D) disease network analysis of large-scale electronic health record (EHR) data. It integrates data harmonization, analysis, and visualization in a single workflow for studying multimorbidity patterns and disease progression pathways. The package supports cohort, matched cohort, and exposed-only cohort designs, and is released under the GPL-3.0 license.

DiNetxify provides:

• Integrated workflow: from cohort data and diagnosis records to analysis outputs and plots.
• Flexible study designs: support for standard cohorts, matched cohorts, and exposed-only cohorts.
• Modular analysis: use the one-step pipeline or run PheWAS, comorbidity, and trajectory analyses separately.
• Built-in visualization: generate static and interactive figures directly from the result tables.

Installation

DiNetxify requires Python 3.10+.

pip install dinetxify

Core dependencies include numpy, pandas, matplotlib, plotly, python_louvain, networkx, scikit_learn, scipy, statsmodels>=0.14.4, lifelines>=0.27.0, and tqdm.

Recent Major Updates

DiNetxify v0.3.0: More reliable data processing and analysis

Following a comprehensive review of the data-processing and analysis workflow, DiNetxify v0.3.0 introduces important corrections to Phecode exclusion, longitudinal EHR processing, PheWAS eligibility and thresholding, comorbidity-strength estimation, RPCN fitting, and time-aware adjustment for disease covariates in trajectory analyses. Together, these updates improve methodological reliability and consistency across analysis stages. See the Changelog in https://hzcohort.github.io/DiNetxify/ and commit history for the complete list of corrections.

DiNetxify v0.2.0: Interactive visualization websites

DiNetxify v0.2.0 introduces Plot.interactive_website(), which converts analysis result tables into a self-contained, multi-page visualization website. The generated site can be opened locally without a web server or internet connection. See the interactive example, which presents results from our previous depression study.

from DiNetxify.visualization import Plot

plot = Plot(
    phewas_result=phewas_result,
    comorbidity_result=com_network_result,
    trajectory_result=trajectory_result,
    exposure_name="MDD diagnosis",
)
plot.interactive_website("results/interactive_website")

The PheWAS section is always generated. Supplying comorbidity_result adds the interactive comorbidity network, while supplying both comorbidity_result and trajectory_result also adds the disease-trajectory and 3D network sections. The website includes filtering, search, tooltips, zoom controls, and figure export where applicable.

Quick Start

1. Load phenotype data and medical records

You can try the package with the dummy data under tests/data.

import DiNetxify as dnt

col_dict = {
    "Participant ID": "ID",
    "Exposure": "exposure",
    "Sex": "sex",
    "Index date": "date_start",
    "End date": "date_end",
}

covariates = ["age", "BMI"]

data = dnt.DiseaseNetworkData(
    study_design="cohort",
    phecode_level=1,
    date_fmt="%Y-%m-%d",
)

data.phenotype_data(
    phenotype_data_path="tests/data/dummy_phenotype.csv",
    column_names=col_dict,
    covariates=covariates,
)

data.merge_medical_records(
    medical_records_data_path="tests/data/dummy_EHR_ICD9.csv",
    diagnosis_code="ICD-9-WHO",
    column_names={
        "Participant ID": "ID",
        "Diagnosis code": "diag_icd9",
        "Date of diagnosis": "dia_date",
    },
)

data.merge_medical_records(
    medical_records_data_path="tests/data/dummy_EHR_ICD10.csv",
    diagnosis_code="ICD-10-WHO",
    column_names={
        "Participant ID": "ID",
        "Diagnosis code": "diag_icd10",
        "Date of diagnosis": "dia_date",
    },
)

2. Run the one-step analysis pipeline

disease_network_pipeline() returns five result tables:

• phewas_result
• com_strength_result
• com_network_result
• binomial_result
• trajectory_result

Example:

from DiNetxify import disease_network_pipeline

if __name__ == "__main__":
    (
        phewas_result,
        com_strength_result,
        com_network_result,
        binomial_result,
        trajectory_result,
    ) = disease_network_pipeline(
        data=data,
        n_process=4,
        n_threshold_phewas=100,
        n_threshold_comorbidity=100,
        output_dir="./results",
        project_prefix="my_analysis",
        keep_positive_associations=True,
        method="RPCN",
        covariates=["age", "BMI"],
        matching_var_dict={"sex": "exact"},
        matching_n=2,
        correction="bonferroni",
        cutoff=0.05,
    )

Notes:

• output_dir must already exist.
• When using multiprocessing, keep the call inside if __name__ == "__main__":.
• For standalone multiprocessing calls on Linux servers, phewas(), comorbidity_network(), and disease_trajectory() support multiprocessing_start_method="forkserver" or another available start method.
• method can be 'RPCN', 'PCN_PCA', or 'CN'.
• For standalone PheWAS, phewas(method="bfgs", maxiter=300) controls the statsmodels Cox optimizer. This is separate from the pipeline method, which selects the network-analysis method.

3. Visualize results

The Plot class can generate individual figures or a complete interactive website. phewas_result is required; comorbidity_result and trajectory_result add the corresponding network views.

Create individual plots

from pathlib import Path

from DiNetxify.visualization import Plot

results_dir = Path("results")
trajectory_dir = results_dir / "trajectory_plots"
trajectory_dir.mkdir(parents=True, exist_ok=True)

plot = Plot(
    phewas_result=phewas_result,
    comorbidity_result=com_network_result,
    trajectory_result=trajectory_result,
    exposure_name="Exposure",
    exposure_location=(0, 0, 0),
    exposure_size=15,
)

plot.phewas_plot(str(results_dir / "phewas_plot.png"))
plot.comorbidity_network_plot(str(results_dir / "comorbidity_network.html"))
plot.trajectory_plot(str(trajectory_dir))
plot.three_dimension_plot(str(results_dir / "three_dimension_network.html"))

When both network result tables are supplied, module assignments are shared across the comorbidity, trajectory, and 3D plots.

Build an interactive website

plot.interactive_website("results/interactive_website")

The generated sections depend on the result tables supplied when constructing Plot:

The PheWAS pages provide system-ordered scatter and selectable forest plots. The network pages provide module and system filters, condition search, labels, tooltips, zoom controls, and figure export. Comorbidity node sizes reflect the configured PheWAS count column, and edge widths reflect the retained association strength, capped at an effect ratio of 10 for display. The site also writes cleaned data payloads and an automatically positioned data/comorbidity.gexf file.

Open the generated index.html in a modern browser. The website bundles its required assets and does not require a web server or internet connection.

To customize disease-system colors, pass SYSTEM=[...] and the corresponding COLOR=[...] list when constructing Plot. Every disease system present in phewas_result must be included in SYSTEM.

For exposed-only cohorts, omit exposure_name, exposure_location, and exposure_size.

Documentation

Full documentation is available at:

https://hzcohort.github.io/DiNetxify/

It includes guides for:

• data preparation and harmonization
• one-step and step-by-step 3D analysis
• visualization
• table generation
• API reference

Citation

If you use this software in your research, please cite:

1. DiNetxify: a python package for three-dimensional disease network analysis based on electronic health record data (PMID: 41579291)
2. Disease clusters and their genetic determinants following a diagnosis of depression: analyses based on a novel three-dimensional disease network approach (PMID: 40681841)

Contact

• Can Hou: houcan@wchscu.cn
• Haowen Liu: haowenliu81@gmail.com