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

Seyednami Niyakan, Jianting Sheng, Yuliang Cao, Xiang Zhang, Zhan Xu, Ling Wu, Stephen T.C. Wong, Xiaoning Qian

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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 languageEnglish (US)
Article number100986
JournalPatterns
Volume5
Issue number5
DOIs
StatePublished - May 10 2024

Keywords

  • Bayesian modeling
  • cellular deconvolution
  • gene expression
  • multi-sample analysis
  • spatial transcriptomics

ASJC Scopus subject areas

  • General Decision Sciences

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