Atlas-level single-cell integration and clustering-free differential expression analysis with GEDI 2.0

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ID: 314938
2026
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Abstract
MOTIVATION: GEDI is a generative framework for multi-sample, multi-condition single-cell analysis that performs batch correction, latent representation learning, and clustering-free differential expression within a unified model. However, the original implementation suffered from prohibitive memory use and runtime, preventing its application to modern atlas-scale datasets. RESULTS: We present GEDI 2.0, a complete high-performance reimplementation featuring a standalone C ++ computational core with pre-allocated workspaces, strict sparse-matrix preservation, optimized BLAS routines, and multi-threaded block-coordinate descent. Across extensive benchmarks spanning up to 500,000 cells and 10,000 features, GEDI 2.0 achieves 40-63.6% mean reduction in peak memory, 2.98× mean single-threaded speedups, and up to 11.5× acceleration with parallel execution, while maintaining full numerical equivalence to the original method. These improvements enable GEDI 2.0 to analyze million-cell datasets, a scale not achievable with the legacy implementation. GEDI 2.0 provides R and Python interfaces and seamless interoperability with common single-cell workflows. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, reproducible codebase, and tutorials are available at https://github.com/csglab/gedi2. SUPPLEMENTARY INFORMATION: Supplementary dataa are available at Bioinformatics online.
Reference Key
openalex_W7162305215 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Arsham Mikaeili Namini, Ali Saberi, Hamed S Najafabadi
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag334
URL
https://doi.org/10.1093/bioinformatics/btag334
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