fastglmpca 0.0.2

Python implementation of fastglmpca [Weine et al., Bioinformatics, 2024] algorithm with PyTorch

fastglmpca

Python implementation of fastglmpca (Weine et al., Bioinformatics, 2024) algorithm with PyTorch backend.

The main concept of fastglmpca is to use a fast iterative algorithm ("Alternative Poisson Regression") to find a low-rank approximation of the input matrix X with a Poisson distribution. It might be used for dimensionality reduction of count data matrices (e.g. scRNA-Seq UMI matrices or nearest neighbours count matrices in Skip-Gram like representations).

The original R package is available at GitHub, this Python package is not an official implementation. In contrast to the original implementation, we don't use line search and instead use a constant learning rate.

Installation

fastglmpca might be installed via pip:

pip install fastglmpca

or the latest development version can be installed from GitHub using:

pip install git+https://github.com/serjisa/fastglmpca

Quck start

fastglmpca works with both sparse and dense matrices. The input matrix X should be a 2D array-like object with shape (n_samples, n_features). The output matrix Z will have shape (n_samples, n_components), where n_components is the number of components to be computed.

import fastglmpca

Z = fastglmpca.poisson(X, n_pcs=10)

Example with scRNA-Seq dataset processing is available in this notebook.

API

Function fastglmpca.poisson has the following parameters:

• X : np.ndarray or torch.Tensor or scipy.sparse matrix Input data matrix of shape (n_samples, n_features).
• n_pcs : int, optional Number of principal components to compute. Default is 30.
• max_iter : int, optional Maximum number of iterations for the optimization algorithm. Default is 1000.
• tol : float, optional Tolerance for convergence of the optimization algorithm. Default is 1e-4.
• col_size_factor : bool, optional Whether to use column size factor in the model. Default is True.
• row_intercept : bool, optional Whether to use row intercept in the model. Default is True.
• verbose : bool, optional Whether to print verbose output during fitting. Default is False.
• device : str or None, optional Device to use for computation. If None, uses "cuda" if available, otherwise "mps" if available, otherwise "cpu". Default is None.
• progress_bar : bool, optional Whether to show a progress bar during fitting. Default is True.
• seed : int or None, optional Random seed for reproducibility. Default is 42.
• return_model : bool, optional Whether to return the fitted model object. Default is False.
• learning_rate : float, optional Step size used in updates. Default is 0.5.
• num_ccd_iter : int, optional Number of cyclic coordinate descent iterations per main iteration to refine factors. Default is 3.
• batch_size_rows : int or None, optional Number of rows for batched computations of expectation terms; tunes memory vs speed. Default uses an adaptive value up to 1024.
• batch_size_cols : int or None, optional Number of columns for batched computations of expectation terms; tunes memory vs speed. Default uses an adaptive value up to 1024.
• init : str, optional Initialization method for factor matrices. 'svd' (default) uses SVD on log1p(X) to produce a strong starting point. 'random' uses small Gaussian noise for LL and FF which can be useful for stress-testing convergence or avoiding SVD costs on extremely large inputs.