DeCoST: unveiling cell type heterogeneity in spatial transcriptomics based on inter-domain alignment and Gaussian kernel conditional autoregressive.

ABSTRACT: Spatial transcriptomics (STs) has emerged as a transformative approach to elucidate cellular heterogeneity and spatial organization within complex tissue microenvironments. However, the analysis of ST data is challenged by limited spatial resolution, resulting in mixed expression profiles at each spatial location. Moreover, the precious spatial information is rarely exploited, and noise issues in spatial transcriptomes (STs) are often overlooked by computational deconvolution methods. In this study, a novel computational framework for STs deconvolution (DeCoST), called DeCoST, is presented. DeCoST capitalizes on the valuable spatial context information by integrating a Gaussian kernel-based conditional autoregressive model. Additionally, the method employs domain adaptation techniques to a

SUBMITTER: Guo X 

PROVIDER: S-EPMC12459264 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature