pyaugur 0.1.0

Python port of Augur: cell type prioritization in high-dimensional single-cell data

pyaugur

Python port of Augur: cell type prioritization in high-dimensional single-cell data.

Install

pip install pyaugur

Quick Start

import numpy as np
import pandas as pd
from pyaugur import calculate_auc

# Expression matrix: genes x cells
expr = pd.read_csv("expression.csv", index_col=0).values
meta = pd.read_csv("metadata.csv")  # columns: cell_type, label

result = calculate_auc(expr, meta=meta)
print(result["AUC"])  # Mean AUC per cell type, ranked

API

calculate_auc(input, meta=None, ...)

Train a classifier (random forest or logistic regression) to predict condition labels per cell type, evaluate AUC in cross-validation.

Returns: dict with AUC (DataFrame), results, feature_importance, parameters.

calculate_differential_prioritization(augur1, augur2, permuted1, permuted2, ...)

Permutation test for differential prioritization between two conditions.

select_variance(mat, var_quantile=0.5)

Feature selection by variance (loess on CV vs mean expression).

select_random(mat, feature_perc=0.5)

Random feature subsampling.

Performance

vs R Augur on sc_sim dataset (15,697 genes x 600 cells):

Metric	Value
Pearson r (AUC parity)	0.9977
Speedup	4.0x

License

GPL-3.0 (matching upstream R package).