Easydecon: Efficient Cell Type Mapping and Deconvolution for High-Definition Spatial Transcriptomic Data
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Motivation
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.
Results
We present Easydecon, a lightweight and modular computational framework for spatial transcriptomics analysis using marker genes from single-cell RNA sequencing data. Easydecon integrates similarity-based label transfer with regression-based deconvolution in a two-phase strategy that first detects expression hotspots and then refines cell type assignments. We demonstrate its efficacy on colorectal cancer datasets, identifying spatially distinct immune and stromal compartments and resolving macrophage subsets with high accuracy. Easydecon supports integration with segmentation tools and outperforms established methods in speed, usability and cell type recovery.
Availability and implementation
Easydecon is implemented in Python, available at https://github.com/sinanugur/easydecon and PyPi.
