PASE

PASE estimates molecular associations within individual patients and summarizes them across a single-cell cohort.

Installation

Use Python 3.10 or later in a fresh environment. From this source checkout:

git clone https://github.com/tjw0613-cell/pase-software.git
cd pase-software
python -m venv .venv
# Windows: .venv\Scripts\activate
# macOS/Linux: source .venv/bin/activate
python -m pip install .

The distribution is named pase-framework, but the Python import is pase. The unrelated audio package named pase on PyPI uses the same import name; do not install both in one environment. This source release does not by itself confirm availability on PyPI.

Five-line example

import pase
adata = pase.datasets.load_gse234933_demo()
res = pase.associate(adata, anchor="LILRB4", target=["MMP14", "MMP9", "SPP1", "APOE"], patient_key="donor", covariates=["log_depth"], cell_mask="myeloid", aggregate="equal", min_cells=30)
print(res.summary())
res.plot_forest()

The bundled GSE234933 subset contains real cells and works without a download. The four-gene target above illustrates the interface; it is not the current manuscript's published 15-gene signature. The complete tutorial is examples/gse234933_quickstart.ipynb. Install the notebook tools with python -m pip install ".[tutorial]", then run jupyter lab examples/gse234933_quickstart.ipynb.

Input format

Supply an AnnData object with cells in rows and unique gene names in adata.var_names. adata.X, or the requested layer, must already contain transformed expression. For count data, the tutorial uses log1p(10000 * count / total_counts) with totals calculated before selecting genes. associate does not normalize raw counts.

Covariates enter a separate least-squares model for each patient, with an intercept. Numeric categorical encodings must be supplied explicitly. Patients absent from the object can be retained using patient_ids. See the input guide for missing values and eligibility rules.

Outputs and choice of summary

res.patients reports every patient, cell counts, observed correlation, eligibility, exclusion reasons and, where applicable, weights or shrunken effects. res.summary() returns one row with the selected method, target, estimate, interval, P value and status. res.metadata records the analysis settings and warnings. res.plot_forest() returns a Matplotlib Axes.

min_cells=30 is adjustable. min_patients=2 is a technical minimum, not a validated recommendation for reliable inference. Set a stricter reporting threshold explicitly, for example min_patients=6. When that threshold is not met, cohort estimates, intervals and P values are withheld, and the patient records remain available. A configurable threshold does not establish its statistical adequacy. See statistical details.

Citation

For this code, cite the authors, PASE software version 1.0.0, and the release URL or version DOI that accompanies the copy used. Machine-readable author information is in CITATION.cff. An earlier analysis archive is available at Zenodo v3; that DOI does not identify this 1.0.0 software release.

Code is distributed under the MIT License. The real-data example retains its source attribution and third-party terms; see THIRD_PARTY_DATA.md.