Bayesian methods have emerged as powerful tools in oncology, particularly in the analysis of complex data derived from bulk and single-cell RNA sequencing. One notable study developed BayesPrism, a Bayesian framework that enables the deconvolution of cell types and gene expression from bulk RNA-seq datasets by leveraging prior information from patient-derived single-cell RNA-seq. This method addresses the challenge of inferring cellular compositions and their contributions to gene expression changes, which is crucial for understanding tumor biology. The authors applied BayesPrism to various cancer types, including primary glioblastoma, head and neck squamous cell carcinoma, and skin cutaneous melanoma, revealing significant correlations between cell type composition and clinical outcomes. The study highlights the importance of integrating single-cell data to enhance the interpretability of bulk RNA-seq results, thus providing a more nuanced understanding of tumor heterogeneity and its implications for patient prognosis (ref: Chu doi.org/10.1038/s43018-022-00356-3/). Furthermore, the research underscores the potential of Bayesian approaches to elucidate spatial heterogeneity in both malignant and nonmalignant cell states, which could inform therapeutic strategies and improve patient management in oncology.