Precise gene expression deconvolution in spatial transcriptomics with STged

Goal of this Work

Spatially resolved transcriptomics (SRT) has transformed tissue biology by linking gene expression profiles with spatial information. However, sequencing-based SRT methods aggregate signals from multiple cell types within capture locations ("spots"), masking cell-type-specific gene expression patterns. Traditional cell type deconvolution methods estimate cell compositions within spots but fail to resolve cell-type-specific gene expression, limiting their ability to uncover critical biological processes such as cellular interactions and microenvironmental dynamics.

Here, we present STged (Spatial Transcriptomic Gene Expression Deconvolution), a novel computational framework that goes beyond traditional deconvolution by reconstructing cell-type-specific gene expression profiles from mixed spots. STged integrates graph-based spatial correlations and reference-derived gene signatures using a non-negative least-squares regression framework, achieving precise and biologically meaningful deconvolution. Comprehensive simulations show that STged consistently outperforms existing methods in accuracy and robustness.

Applications to human pancreatic cancer and squamous cell carcinoma datasets reveal its capacity to identify microenvironment-specific highly variable genes, reconstruct spatial cell-cell communication networks, and resolve tissue architecture at near-single-cell resolution. In mouse kidney tissues, STged uncovers dynamic spatial gene expression patterns and distinct gene programs, advancing our understanding of tissue heterogeneity and cellular dynamics.

Type of Folders

Here, we provide the data used in the manuscript along with the corresponding results from simulation studies and real data analysis. There are two folders:

• The folder named realdata contains the raw data used in STged.  
• The folder named results contains the results generated by STged and the compared methods.