bibentry(
  bibtype = "Article",
  title = "Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis",
  author = c(
    person("Xiyu", "Peng"),
    person("James W.", "Smithy"),
    person("Mohammad", "Yosofvand"),
    person("Caroline E.", "Kostrzewa"),
    person("MaryLena", "Bleile"),
    person("Fiona D.", "Ehrich"),
    person("Jasme", "Lee"),
    person("Michael A.", "Postow"),
    person("Margaret K.", "Callahan"),
    person("Katherine S.", "Panageas"),
    person("Ronglai", "Shen")
  ),
  journal = "Nature Communications",
  year = "2025",
  volume = "16",
  number = "1",
  pages = "6619",
  doi = "10.1038/s41467-025-61821-y",
  url = "https://doi.org/10.1038/s41467-025-61821-y",
  textVersion = paste(
    "Peng X, Smithy JW, Yosofvand M, Kostrzewa CE, Bleile M, Ehrich FD,",
    "Lee J, Postow MA, Callahan MK, Panageas KS, Shen R.",
    "Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis.",
    "Nature Communications. 2025;16:6619.",
    "doi:10.1038/s41467-025-61821-y"
  )
)