Topology-adjusted knockout (TAKO): interpretable in silico single-gene knockout ranking on wild-type single-cell gene regulatory networks.
- 1Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan.
- 2Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, Japan.
Abstract
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.
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BibTeX
@article{wang2026topologyadjusted,
title = {{Topology-adjusted knockout (TAKO): interpretable in silico single-gene knockout ranking on wild-type single-cell gene regulatory networks.}},
author = {Hao Wang and Koji Oba},
journal = {Frontiers in bioinformatics},
year = {2026},
month = oct,
doi = {10.3389/fbinf.2026.1924703},
url = {https://europepmc.org/article/MED/42953009},
note = {CC BY},
}
RIS
TY - JOUR
TI - Topology-adjusted knockout (TAKO): interpretable in silico single-gene knockout ranking on wild-type single-cell gene regulatory networks.
AU - Hao Wang
AU - Koji Oba
PY - 2026
DA - 2026/10/14
JO - Frontiers in bioinformatics
DO - 10.3389/fbinf.2026.1924703
UR - https://europepmc.org/article/MED/42953009
AB - 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.
SN - 2673-7647
ER -