This repository implements SPECTRA (Supervised discovery of interpretable gene programs from single-cell data), a PyTorch-based library for analyzing single-cell RNA sequencing data. The core functionality involves fitting a model that combines gene expression data with prior knowledge from gene sets to discover biologically interpretable factors. The system takes an AnnData object containing single-cell expression data, cell type annotations, and a gene set dictionary, then learns factors representing gene programs associated with cellular processes. Key features include cell-type specific factor modeling, GPU acceleration options, and multiple optimization approaches (standard gradient descent and EM variants). The codebase demonstrates good software engineering practices with separable components for utility functions, initialization strategies, and eigenvalue-based factor estimation.