RT Journal Article SR Electronic T1 STAN, a computational framework for inferring spatially informed transcription factor activity JF bioRxiv FD Cold Spring Harbor Laboratory SP 2024.06.26.600782 DO 10.1101/2024.06.26.600782 A1 Zhang, Linan A1 Sagan, April A1 Qin, Bin A1 Kim, Elena A1 Hu, Baoli A1 Osmanbeyoglu, Hatice Ulku YR 2024 UL http://biorxiv.org/content/early/2024/09/03/2024.06.26.600782.abstract AB Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN (Spatially informed Transcription factor Activity Network), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.Competing Interest StatementThe authors have declared no competing interest.