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bioRxiv · 10.64898/2026.05.07.723486

Spurious correlation inflates performance in single-cell perturbation prediction

Nicol, P. B.·Shivakumar, S.·Irizarry, R.
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Abstract

The increasing number of computational methods designed to predict the effects of genetic perturbations on cellular gene expression profiles has led to a need for rigorous evaluation metrics. Recent benchmarking studies rely on correlation or cosine similarity of differential expression relative to a shared population of control cells. We show that these metrics are systematically inflated by statistical bias induced by reusing the same control population to define both quantities being compared. As a result, even non-informative methods can appear to perform well, particularly in datasets with limited numbers of control cells. Reanalysis of published datasets using a simple control-splitting procedure that removes this bias leads to a substantial reduction in performance previously attributed to biological signal.

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Nicol, P. B., Shivakumar, S., Irizarry, R.. 2026-05-12. Spurious correlation inflates performance in single-cell perturbation prediction. https://doi.org/10.64898/2026.05.07.723486

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