Deconvolution of cell types and states in spatial multiomics utilizing TACIT
Abstract Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology.While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease.To address this, we develop TACIT, an unsupervised algorithm for cell an