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Peer-reviewed journal article

MUSTANG: MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance

Seyednami NiyakaniD, Jianting ShengiD, Yuliang Cao, Xiang H.-F. ZhangiD, Zhan Xu, Ling WuiD, Stephen T. C. WongiD, Xiaoning QianiD

Abstract

AO_SCPLOWBSTRACTC_SCPLOWSpatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. We here 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 two real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in cellular characterization of tissue samples under the study. MUSTANG is publicly available at at https://github.com/namini94/MUSTANG

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