glmpca-fast 0.1.1

Fast GLM-PCA (Townes et al. 2019) with Rust core and optional PyTorch GPU backend

glmpca-fast

Fast GLM-PCA (Generalised Linear Model PCA) for non-Gaussian count data — Rust core with optional PyTorch GPU backend.

Implements the algorithm of:

Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019). "Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model." Genome Biology, 20:295. doi:10.1186/s13059-019-1861-6

Why

Standard PCA assumes Gaussian + homoscedastic noise — wrong likelihood for count data (RNA-seq UMIs, genotype dosages, etc.). GLM-PCA fits the proper Poisson / Multinomial / Bernoulli / NB likelihood, capturing the mean–variance relationship inside the model.

The reference implementation (willtownes/glmpca-py) is CPU-only Python. glmpca-fast ports the algorithm to:

• Rust (rayon-parallel coordinate-block Newton) — ~13× faster than glmpca-py on a single CPU.
• PyTorch with batched torch.linalg.solve — ~290× faster on a modern GPU (RTX A6000) per fit.

Install

With uv (recommended)

# Add to a project (preferred)
uv add glmpca-fast                    # CPU (Rust + numpy)
uv add "glmpca-fast[torch]"           # + GPU (PyTorch)

# Or install into the active environment
uv pip install glmpca-fast
uv pip install "glmpca-fast[torch]"

# Or run a one-shot script
uv tool run --from glmpca-fast python -c "from glmpca_fast import fit_poisson; ..."

With pip

pip install glmpca-fast              # CPU
pip install "glmpca-fast[torch]"     # + GPU

Quick start

import numpy as np
from glmpca_fast import fit_poisson

# Synthetic Binomial(2, p) genotype dosage matrix
rng = np.random.default_rng(0)
N, M, L = 2504, 200, 8
p = rng.uniform(0.05, 0.5, M)
Y = rng.binomial(2, p, size=(N, M)).astype(np.float32)

# Rust backend (default, CPU)
res = fit_poisson(Y, L=L, max_iter=100)
print(res["factors"].shape)         # (2504, 8)
print(res["loadings"].shape)        # (200, 8)
print(res["deviance"][-1])          # final deviance
print(res["backend"])               # 'rust'

# Auto backend — picks GPU if CUDA is available
res = fit_poisson(Y, L=L, backend="auto")
print(res["backend"])               # 'torch' if CUDA, else 'rust'

# Explicit GPU device
res = fit_poisson(Y, L=L, backend="torch", device="cuda:0")

API

fit_poisson(
    Y,                   # (n_samples, n_features) non-negative counts
    L,                   # latent dim, >= 2
    max_iter=100,
    tol=1e-4,            # relative deviance tolerance
    penalty=1.0,         # L2 ridge on factors and loadings
    seed=42,
    backend="rust",      # 'rust' | 'torch' | 'auto'
    device=None,         # torch device override (e.g. 'cuda:1')
) -> dict
# returns: factors, loadings, intercept, deviance, n_iter, backend

project_ols(X_held, train_mean, loadings) -> ndarray
# Approximate OLS projection of held-out samples (Pearson-residual approx).

Benchmark

Single gene, 2,504 samples × 200 variants, L=8:

Backend	Time / fit	Speedup vs glmpca-py
glmpca-py (reference, NumPy)	7.58 s	1×
glmpca-fast (Rust, 11 cores)	0.56 s	13.5×
glmpca-fast (PyTorch, RTX A6000)	0.026 s	290×

Both backends converge to within ~1 % of the reference final deviance (non-convex objective, different random init).

Limitations / scope

• Currently Poisson family only. Multinomial / NB / Bernoulli branches are planned for v0.2.
• Newton step uses full update without line-search damping. For degenerate Hessians the implementation falls back to a small gradient step.
• Held-out projection uses the OLS approximation (not full per-sample IRLS).
• Built and tested on Linux x86-64 + CUDA 12. Other platforms via source build.

Citation

If you use this package, please cite both the original paper and this software:

@article{Townes2019GLMPCA,
  title   = {Feature selection and dimension reduction for single-cell
             RNA-Seq based on a multinomial model},
  author  = {Townes, F. William and Hicks, Stephanie C. and Aryee,
             Martin J. and Irizarry, Rafael A.},
  journal = {Genome Biology},
  volume  = {20},
  number  = {1},
  pages   = {295},
  year    = {2019},
  doi     = {10.1186/s13059-019-1861-6}
}

@software{glmpca_fast,
  title  = {glmpca-fast: Fast GLM-PCA with Rust and GPU backends},
  author = {zongseung},
  year   = {2026},
  url    = {https://github.com/zongseung/glmpca-fast}
}

License

MIT — see LICENSE.