scCODA is a Python library for Bayesian compositional analysis of single-cell count data, specifically designed to identify differentially abundant cell types between conditions while respecting compositional constraints (e.g., cell type proportions sum to 1). The core implements a Dirichlet-Multinomial model with spike-and-slab priors, providing posterior inclusion probabilities and effect size estimates. It integrates with the scverse ecosystem (anndata, scanpy) for data handling and uses TensorFlow Probability for HMC/NUTS sampling. The package also includes wrappers for alternative methods (ALDEx2, ANCOM, etc.) and provides utilities for data conversion, visualization, and synthetic data generation. While the repository is well-architected with clear separation of concerns and includes tutorials and unit tests, it is explicitly marked as no longer maintained (successor in pertpy using JAX). The technical drift is high due to outdated TensorFlow/TPP dependencies and lack of active development.