scUGPT: Gene-wise Response Curves Along a Supplied Single-Cell Trajectory Axis

A central goal in functional genomics is to predict not only how strongly a perturbation shifts a cell, but when each gene responds. Single-cell measurements are noisy and sparse, and a response is observed at only a few experimental time points. Most perturbation models predict gene expression at a measured time point, so they do not directly describe response onset or persistence. We present \method, a trajectory-conditioned model that predicts gene-wise response curves along a supplied process axis. An analytic process kernel separates response centre, persistence, and late switching, distinguishing transient and maintained programmes in the parameterization. Perturbation-conditioned attention and a residual prediction path refine the kernel when the observed grid is too sparse to identify its parameters. On the reference GSE213069 split, anchor calibration improves full-curve reconstruction and the model reaches day-4-to-day-5 change correlation $0.261\pm 0.041$, versus $0.156\pm 0.032$ without the residual path. Across four additional condition splits, change correlation averages $0.007\pm 0.122$. The results support a structured representation of response curves, with forecasting performance that depends on the condition split. Code is available at https://osf.io/xn3fh/?view_only=dc7333b3d4d74a0aa1baaf9f964306bf.