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.
MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance.
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作者:Niyakan Seyednami, Sheng Jianting, Cao Yuliang, Zhang Xiang, Xu Zhan, Wu Ling, Wong Stephen T C, Qian Xiaoning
| 期刊: | Patterns | 影响因子: | 7.400 |
| 时间: | 2024 | 起止号: | 2024 May 2; 5(5):100986 |
| doi: | 10.1016/j.patter.2024.100986 | ||
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