doi: https://doi.org/10.1101/2023.05.25.542052

Abstract

Spatial transcriptomics is a cutting-edge technique that combines gene expression data with spatial information, allowing researchers to study gene expression patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment-anything model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

Authors

Lee D, Park J, Cook S, Yoo S, Lee D, Choi H.(2023)