AI RESEARCH
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
arXiv CS.LG
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ArXi:2605.20989v1 Announce Type: new Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fundamental challenge. Current approaches utilizing neural differential equations and flows are sensitive to overfitting and lack careful considerations of biological variability. In this work, we propose a generative framework that models population trends using a latent heteroscedastic Gaussian process (GP) approximated by Hilbert space methods.