Topology-adjusted knockout (TAKO): interpretable in silico single-gene knockout ranking on wild-type single-cell gene regulatory networks

Predicting single-gene knockout effects from wild-type single-cell RNA-seq data remains challenging when perturbation experiments are costly and existing approaches require knockout-specific model training. Here we introduce Topology-Adjusted Knockout (TAKO), a computationally efficient WT-only framework for single-gene KO impact ranking on single-cell gene regulatory networks. The method applies a strict cut-in-and-out intervention on network transition dynamics and quantifies impact by contrasting stationary propagation profiles before and after perturbation under the same restart distribution. In SERGIO simulations, TAKO recovered direct regulator-target structure but did not show a consistent global advantage over a matched absolute Pearson-correlation ranking across network sizes. Direct evaluation against measured responses in Trem2 and Nkx2-1 KO data likewise yielded mixed global discrimination, although TAKO contained more differentially expressed genes among the top-ranked candidates in both examples. These results position TAKO as a fast, interpretable topology-based prioritisation method rather than a generally superior predictor of post-KO expression responses.