Discussion
cancer, tumor, tumors
CANCER, TUMOR, TUMORS, CANCER PROGRESSION
We present SpaCET with several algorithmic designs to address the challenge of decomposing cell fractions in tumor ST data. We demonstrated its superior performance over existing deconvolution methods on ST data with a broad range of cellular resolutions. SpaCET estimates cancer cell fractions based on a gene pattern dictionary of copy number alterations and expression changes across tumor types, which performs better than the inferCNV-based strategyOur strategy of hierarchical decomposition constrains the negative effect of collinearity among closely related cell types within a sublineage of decomposition results (Supplementary Fig. The spatial heterogeneity in different tumor regions is driven by tumor evolution and intercellular interactions between cancer cells and immune/stromal cellsSeveral limitations exist for SpaCET. First, when estimating the malignant cell fractions (Fig. SpaCET is a framework for understanding the spatial organization of cells in tumors and how spatial organizations influence cancer progression. With the continuous accumulation of spatial transcriptomics data from clinical studies, we foresee that SpaCET will provide mechanistic insights underlying many oncogenic processes and therapeutic solutions to bottlenecks of current antitumor treatments.