A Standardized Framework For Evaluating Gene Expression Generative Models
The rapid development of generative models for single-cell gene expression data has created an urgent need for standardised evaluation frameworks. Current evaluation practices suffer from inconsistent metric implementations, incomparable hyperparameter choices, and a lack of biologically-grounded metrics. We present Generated Genetic Expression Evaluator (GGE), an open-source Python framework that addresses these challenges by providing a comprehensive suite of distributional metrics with explicit computation space options and biologically-motivated evaluation through differentially expressed gene (DEG)-focused analysis and perturbation-effect correlation, enabling standardized reporting ...
| Authors | Andrea Rubbi, Andrea Giuseppe Di Francesco, Mohammad Lotfollahi et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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