A Pitfall in the Evaluation of Gene Perturbation Models
Gene perturbation models aim to predict transcriptional responses to unseen genetic interventions, but comparisons rely on heterogeneous evaluation metrics and differentially-expressed (DE) gene subsets. We investigate the consequences of these choices using eight methods across six single-gene perturbation datasets under a unified evaluation protocol. Varying the number of top DE genes reveals substantial instability in Precision-based rankings, with the winning method changing $86.7\%$ of the time, while rankings based on the rest of the metrics remain comparatively stable. Investigating this discrepancy exposes that repeating a single predicted expression profile across cell-level rows...
| Authors | George Panagopoulos, Saman Omidi, Tsz Pan Tong et al. |
|---|---|
| Primary category | stat.AP |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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