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MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance
Seyednami Niyakan1, Jianting Sheng2, Yuliang Cao2
1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
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.
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