Abstract
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.
Original language | English (US) |
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Article number | 100986 |
Journal | Patterns |
Volume | 5 |
Issue number | 5 |
DOIs | |
State | Published - May 10 2024 |
Keywords
- Bayesian modeling
- cellular deconvolution
- gene expression
- multi-sample analysis
- spatial transcriptomics
ASJC Scopus subject areas
- General Decision Sciences