A unified model to detect spatially-variable and trajectory-preserved genes in spatial transcriptomics

Music City Center 

Identifying spatially variable genes (SVGs) has been an essential task in spatial transcriptomics. In addition to SVGs detection, there are gene expressions showing developmental patterns or spatial trajectories across a tissue section. Identifying such genes could provide novel insights into tumor metastasis. Here, we introduce a unified statistical model to detect both types of genes. In addition, we propose a novel method to address the inherent double dipping problem commonly encountered when assessing temporal gene effect in single-cell sequencing studies. We demonstrate the testing performance through extensive simulation studies and through analyses of several publicly available datasets. Downstream analyses further highlight the potential of our method in identifying genes associated with tumor progression and enhancing domain detection.