Easydecon: Efficient Cell Type Mapping for High-Definition Spatial Transcriptomic Data

Abstract

The emergence of high-resolution spatial transcriptomics platforms, such as VisiumHD, has enabled transcriptome-wide spatial profiling at near-single-cell resolution. However, existing analysis tools often lack scalability or compatibility with this new resolution, limiting their utility for multimodal cell type analysis. We present Easydecon, a lightweight and modular computational framework for spatial transcriptomics analysis using marker genes from single-cell RNA sequencing datasets. Easydecon uses a two-phase strategy, which firstly detects expression hotspots and then refines cell type assignments with similarity-based methods. We demonstrate its efficacy by resolving cell type subsets with high accuracy. Easydecon supports integration with segmentation tools and outperforms established methods in speed, usability and cell type recovery.