# scGPT

In single-cell foundation models, predicting the effect of overexpressing genes had been limited to one gene at a time or to enumerating a few hundred combinations. A Kyushu Institute of Technology team recast choosing which genes to overexpress as a discrete adversarial attack on gene tokens and searched combinations with Greedy Coordinate Gradient (GCG), a search built for language models. In Geneformer and scGPT, GCG raised similarity to the target cell state above gradient-pool greedy at equal forward-pass cost, with the margin growing with set size; measured against published reprogramming factors, however, ranking by single-gene effect recovered more of them.

In single-cell foundation models, predicting the effect of overexpressing genes had been limited to one gene at a time or to enumerating a few hundred combinations. A Kyushu Institute of Technology team recast choosing which genes to overexpress as a discrete adversarial attack on gene tokens and searched combinations with Greedy Coordinate Gradient (GCG), a search built for language models. In Geneformer and scGPT, GCG raised similarity to the target cell state above gradient-pool greedy at equal forward-pass cost, with the margin growing with set size; measured against published reprogramming factors, however, ranking by single-gene effect recovered more of them.

Single-cell perturbation experiments are costly and low-throughput, yet computational prediction has been hampered by inconsistent evaluation and by models that do not reliably beat simple baselines. DCAF combines data pairing, an evaluation criterion and a prediction model in one framework: entropy-regularized optimal transport recovers a pre-perturbation state for each perturbed cell, log fold changes of non-silenced genes drive scoring, and a JEPA structure encodes control and perturbed cells jointly. Across cross-gene, cross-cell-type and cross-region settings, DCAF-model beats GEARS, scGPT, linear and mean baselines on R² and MSE of log fold change for non-silenced genes.

Single-cell perturbation experiments are costly and low-throughput, yet computational prediction has been hampered by inconsistent evaluation and by models that do not reliably beat simple baselines. DCAF combines data pairing, an evaluation criterion and a prediction model in one framework: entropy-regularized optimal transport recovers a pre-perturbation state for each perturbed cell, log fold changes of non-silenced genes drive scoring, and a JEPA structure encodes control and perturbed cells jointly. Across cross-gene, cross-cell-type and cross-region settings, DCAF-model beats GEARS, scGPT, linear and mean baselines on R² and MSE of log fold change for non-silenced genes.