Abstract
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular heterogeneity, yet most datasets are limited to a handful of model organisms, leaving critical gaps in cross-species biology. Existing computational methods for integrating multi-species scRNA-seq data often rely on one-to-one ortholog mapping, which fails to account for gene duplications, losses, or functional divergences. To address these challenges, we introduce CycloCross, an adversarial method based on the CycleGAN architecture, designed to translate scRNA-seq data between species without requiring a priori gene correspondences. Unlike generative models that synthesize data from noise, CycloCross leverages real data from a source species, enabling more realistic and data-efficient translations. We demonstrate its effectiveness in germ cell development, translating data between mouse, macaque, and human with conserved yet divergent transcriptional programs. CycloCross not only preserves temporal dynamics but also enables interpolation and extrapolation of gene expression at missing time points, offering predictions for experimentally inaccessible states. Furthermore, CycloCross generates biologically plausible samples even with limited data, outperforming traditional generative models.