MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance

著者: Seyednami Niyakan, Jianting Sheng, Yuliang Cao, Xiang H.-F. Zhang, Xu Zhan, Ling Wu, Stephen T.C. Wong, Xiaoning Qian - Patterns 2024 被引用: 10

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.