Reproducibility

SecActPy supports three RNG backends via the rng_method parameter, each offering different trade-offs between R compatibility and performance:

rng_method	Description	Use case
'srand'	C stdlib srand()/rand() via ctypes	Match R SecAct (with RidgeFast/RidgeCuda accelerators) results on the same platform
'gsl'	Mersenne Twister (GSL-compatible)	Cross-platform reproducibility within SecActPy
'numpy'	Native NumPy RNG (~70x faster)	Fast analysis when reproducibility with R is not needed

Matching R SecAct output (with RidgeFast/RidgeCuda accelerators)

To reproduce R SecAct results on the same machine, use rng_method='srand'. This uses the C standard library's rand() function, which matches R's internal RNG on the same platform. Note that C rand() implementations differ across operating systems, so results are platform-dependent.

result = secact_activity_inference(
    expression,
    is_differential=True,
    sig_matrix="secact",
    lambda_=5e5,
    n_rand=1000,
    seed=0,
    rng_method="srand",  # Match R SecAct on same platform
)

Cross-platform reproducibility

The rng_method='gsl' backend uses a portable Mersenne Twister implementation that produces identical results across all platforms (Linux, macOS, Windows). This does not match R output, but guarantees consistent SecActPy results everywhere.

result = secact_activity_inference(
    expression,
    rng_method="gsl",  # Cross-platform reproducible
)

Fastest analysis

For maximum throughput when reproducibility is not required:

result = secact_activity_inference(
    expression,
    rng_method="numpy",  # ~70x faster permutation generation
)