Eykthyr reveals transcriptional regulators of spatial gene programs.

Understanding how transcription factors (TFs) orchestrate gene regulatory networks that define complex tissue structures is central to uncovering tissue organization and disease mechanisms. Although spatial multiome technologies now enable measurement of both transcriptional activity and chromatin accessibility, existing computational methods either overlook spatial tissue context or are hindered by the high dropout rates characteristic of such data. Here, we introduce Eykthyr, a computational framework that integrates gene expression and chromatin accessibility within a spatially aware model to identify TFs driving spatial gene programs. Eykthyr mitigates dropout effects by leveraging interpretable, low-dimensional embeddings of gene expression and chromatin accessibility - both linear with respect to their input - enabling robust identification and scalable inference of spatial transcriptional regulators. Applied across diverse spatial multiome datasets, Eykthyr consistently outperforms existing approaches, accurately identifying TFs that coordinate spatial gene programs in mouse brain development and regulate T-cell states within tumor microenvironments. Eykthyr establishes a foundation for decoding how TFs interpret local intercellular signaling to shape tissue structure, offering insights into the regulatory logic underlying spatial organization in health and disease.