Introduction
Tumors, cancer, tumor
TUMORS, CANCER, TUMOR PROGRESSION, TUMOR
Profiling the transcriptome of cells in their spatial context is critical to a mechanistic understanding of tumor progression and therapeutic resistanceMany methods exist for cell type decomposition in general ST dataAnother strategy to deconvolve cell fractions is to generate the single-cell RNA sequencing (scRNA-seq) data paired with ST data on the same tumor sampleThis study introduces a computational framework, SpaCET (Spatial Cellular Estimator for Tumors), to decompose cell identities in tumor ST data. SpaCET addresses the challenges of tumor heterogeneities, tissue density variations, immune cell integrity, and collinearity among sublineages, which are not sufficiently considered in existing deconvolution methods. SpaCET outperforms other methods on eight tumor ST datasets spanning seven cancer types based on double-blind histopathology annotations. Moreover, SpaCET uncovered several potential cell–cell interactions supporting tumor progression. The source code for SpaCET is publicly available at 