ST-Assign: a probabilistic model for joint cell type identification in spatial transcriptomics and single-cell RNA sequencing data

Abstract

AbstractWe present ST-Assign, a novel computational tool for joint cell-type annotation in single-cell RNA sequencing and cell-type mixture decomposition in spatial transcriptomics data. The model integrates the two data sources to enhance cell-type identification. It accounts for shared variables such as gene expression profiles and leverages prior knowledge about marker genes. We formulate the model as a generative graph-based structure and employ Markov chain Monte Carlo for the inference. We demonstrate the model’s utility on mouse brain data, achieving simultaneous cell-type annotation and decomposition of cell-type mixtures. ST-Assign provides valuable insights into cell populations within organisms, improving our understanding of cellular heterogeneity.