Your browser doesn't support javascript.
loading
MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance.
Niyakan, Seyednami; Sheng, Jianting; Cao, Yuliang; Zhang, Xiang; Xu, Zhan; Wu, Ling; Wong, Stephen T C; Qian, Xiaoning.
Afiliação
  • Niyakan S; Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
  • Sheng J; Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, TX 77030, USA.
  • Cao Y; Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, TX 77030, USA.
  • Zhang X; Lester and Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
  • Xu Z; Lester and Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
  • Wu L; Lester and Sue Smith Breast Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
  • Wong STC; Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, TX 77030, USA.
  • Qian X; Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Patterns (N Y) ; 5(5): 100986, 2024 May 10.
Article em En | MEDLINE | ID: mdl-38800365
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.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Patterns (N Y) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Patterns (N Y) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos
...