Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction

• URL: http://arxiv.org/abs/2602.04901v1
• Date: Tue, 03 Feb 2026 16:43:40 GMT
• Title: Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction
• Authors: Jiafa Ruan, Ruijie Quan, Zongxin Yang, Liyang Xu, Yi Yang,
• Abstract summary: scBIG is a module-inductive prediction framework that explicitly models coordinated gene programs.<n> scBIG consistently outperforms state-of-the-art methods, particularly on unseen and perturbation settings.
• Score: 48.80217316452559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics. In practice, perturbation responses are rarely gene-independent but instead manifest as coordinated, program-level transcriptional changes among functionally related genes. However, most existing methods do not explicitly model such coordination, due to gene-wise modeling paradigms and reliance on static biological priors that cannot capture dynamic program reorganization. To address these limitations, we propose scBIG, a module-inductive perturbation prediction framework that explicitly models coordinated gene programs. scBIG induces coherent gene programs from data via Gene-Relation Clustering, captures inter-program interactions through a Gene-Cluster-Aware Encoder, and preserves modular coordination using structure-aware alignment objectives. These structured representations are then modeled using conditional flow matching to enable flexible and generalizable perturbation prediction. Extensive experiments on multiple single-cell perturbation benchmarks show that scBIG consistently outperforms state-of-the-art methods, particularly on unseen and combinatorial perturbation settings, achieving an average improvement of 6.7% over the strongest baselines.

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