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

Identifying spatially variable genes (SVGs) has been an essential task in spatial transcriptomics. In addition to SVGs detection, there are genes exhibiting expression patterns that are associated with cellular developmental stages or lineage fates across a tissue section. Identifying such genes could provide novel insights into tumor metastasis. Here, we introduce a unified statistical model, termed TrajEctory-preServed and SpAtially-variable gene detection (TESSA), to detect both types of genes. Moreover, we propose a novel strategy to address the inherent double-dipping issue commonly encountered when assessing temporal gene effects in transcriptomics analysis. We demonstrate the testing performance through extensive simulation studies and real applications to several publicly available datasets. Downstream analyses further highlight the potential of our method in identifying genes associated with tumor progression and enhancing spatial domain detection.