Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations

• URL: http://arxiv.org/abs/2606.24940v1
• Date: Mon, 22 Jun 2026 19:48:07 GMT
• Title: Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations
• Abstract summary: Stable-Shift is a structured method for estimating unseen-gene responses.<n>System combines STRING interactions, network structure, control-cell expression statistics, and Gene Ontology annotations.<n>Mean cosine similarity over five unseen-gene splits was 0.589 +/- 0.008.
• Score: 8.089217380596391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes that were never perturbed during training remains difficult. We present Stable-Shift, a structured method for estimating unseen-gene responses. Stable-Shift aggregates single-cell measurements into perturbation-level expression shifts, fits a low-rank response basis using training perturbations only, and predicts an unseen gene's coordinates in that basis from biological context. The context combines STRING interactions, network structure, control-cell expression statistics, and Gene Ontology annotations; the evaluated implementation uses graph convolution to integrate these inputs. On the supplied K562 Perturb-seq benchmark, Stable-Shift obtained 0.592 cosine similarity, compared with 0.569 for GEARS, together with higher Spearman correlation and top-gene precision among the evaluated methods. Its mean cosine similarity over five unseen-gene splits was 0.589 +/- 0.008. The same ordering was observed in the supplied graph-aware, residualized, gene-space, and Norman-dataset comparisons. These results support further study of biologically structured latent-response prediction, while the lower gene-space accuracy and sensitivity to sparse graph neighborhoods limit the scope of the present conclusions.

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