PT  - JOURNAL ARTICLE
AU  - Zhang, Linan
AU  - Sagan, April
AU  - Qin, Bin
AU  - Kim, Elena
AU  - Hu, Baoli
AU  - Osmanbeyoglu, Hatice Ulku
TI  - STAN, a computational framework for inferring spatially informed transcription factor activity
AID  - 10.1101/2024.06.26.600782
DP  - 2024 Jan 01
TA  - bioRxiv
PG  - 2024.06.26.600782
4099  - http://biorxiv.org/content/early/2024/09/03/2024.06.26.600782.short
4100  - http://biorxiv.org/content/early/2024/09/03/2024.06.26.600782.full
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.