cytoGateR Python

This is the Python implementation of cytoGateR. It uses an AnnData object and keeps the R function names, parameter names, and defaults where Python syntax permits.

Implemented modules include:

• marker GMMs, soft gating, and tree gating;
• hierarchical reference construction and recursive hierarchical kNN;
• weighted kNN consensus cleaning and prediction;
• random-forest consensus cleaning and prediction;
• optional PyTorch training and prediction;
• confidence labels, cutoffs, F1, confusion, and agreement metrics;
• probability and spatial uncertainty;
• Matplotlib spatial, marker, label, metric, and tree plots.

Installation

Install the latest release from PyPI:

pip install cytogater

Plotting, neural-network, and notebook support are available as optional extras:

pip install 'cytogater[plot]'
pip install 'cytogater[torch]'
pip install 'cytogater[notebook]'

To install from source for development, clone the repository and create the provided Conda environment:

git clone https://github.com/SydneyBioX/cytoGateR.git
cd cytoGateR/python
conda env create -f environment.yml
conda activate cytogater-python
pytest

Data layout

Python AnnData stores cells in rows and markers in columns. Put the expression matrix used by the R assay_name = "exprs" default in:

adata.layers["exprs"]

The lineage table is a pandas data frame with cell_type, pos_markers, and neg_markers columns. The marker columns contain Python lists.

Example

import pandas as pd
import cytogater

lineage_table = pd.DataFrame({
    "cell_type": ["Tcell", "Bcell"],
    "pos_markers": [["CD3e"], ["CD20"]],
    "neg_markers": [["CD20"], ["CD3e"]],
})

result = cytogater.run_tree_gating(
    adata,
    lineage_table,
    assay_name="exprs",
    max_depth=1,
    min_cells=100,
    min_score=0.1,
    workers=1,
)

result["hard_label"]
result["prob_mat"]

Results are returned with the same main keys as R. Labels and P_* columns are also stored in adata.obs; probability matrices are stored in adata.obsm.

The package is intentionally flat at its public surface:

cytogater.run_soft_gating(...)
cytogater.run_tree_gating(...)
cytogater.train_custom_knn(...)
cytogater.train_custom_randomforest(...)
cytogater.calculate_uncertainty(...)

The implementations are also grouped under cytogater.gating, cytogater.models, cytogater.metrics, cytogater.uncertainty, and cytogater.plotting.

R compatibility note

The Python implementation uses a deterministic two-component Gaussian-mixture fit. R uses mclust, so fitted cutoffs can differ slightly even though the pipeline, parameters, defaults, scoring, and output structure are aligned. The Python lambda_ argument corresponds to the R lambda argument because lambda is reserved syntax in Python. num_trees corresponds to R's num.trees; train_custom_randomforest() also accepts **{"num.trees": 200}.

Run tests with:

cd cytoGateR/python
pytest