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DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices.
Masotti, Maria; Osher, Nathaniel; Eliason, Joel; Rao, Arvind; Baladandayuthapani, Veerabhadran.
Afiliación
  • Masotti M; University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA.
  • Osher N; University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA.
  • Eliason J; University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA.
  • Rao A; University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA.
  • Baladandayuthapani V; University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA.
Patterns (N Y) ; 4(12): 100879, 2023 Dec 08.
Article en En | MEDLINE | ID: mdl-38106614
ABSTRACT
A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Patterns (N Y) Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Patterns (N Y) Año: 2023 Tipo del documento: Article