NIPMAP: niche-phenotype mapping of multiplex histology data by community
ecology.
ecology.
Advances in multiplex histology allow surveying millions of cells,
dozens of cell types, and up to thousands of phenotypes within the
spatial context of tissue sections. This leads to a combinatorial
challenge in (a) summarizing the cellular and phenotypic architecture of
tissues and (b) identifying phenotypes with interesting spatial
architecture.
To address this, we combine ideas from community ecology and machine
learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage
of geometric constraints on local cellular composition imposed by the
niche structure of tissues in order to automatically segment tissue
sections into niches and their interfaces. Projecting phenotypes on
niches and their interfaces identifies previously-reported and novel
spatially-driven phenotypes, concisely summarizes the phenotypic ar-
chitecture of tissues, and reveals fundamental properties of tissue
architecture.
NIPMAP is applicable to both protein and RNA multiplex histology of
healthy and diseased tissue. An open-source R/Python package implements
NIPMAP. https://github.com/jhausserlab/
dozens of cell types, and up to thousands of phenotypes within the
spatial context of tissue sections. This leads to a combinatorial
challenge in (a) summarizing the cellular and phenotypic architecture of
tissues and (b) identifying phenotypes with interesting spatial
architecture.
To address this, we combine ideas from community ecology and machine
learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage
of geometric constraints on local cellular composition imposed by the
niche structure of tissues in order to automatically segment tissue
sections into niches and their interfaces. Projecting phenotypes on
niches and their interfaces identifies previously-reported and novel
spatially-driven phenotypes, concisely summarizes the phenotypic ar-
chitecture of tissues, and reveals fundamental properties of tissue
architecture.
NIPMAP is applicable to both protein and RNA multiplex histology of
healthy and diseased tissue. An open-source R/Python package implements
NIPMAP. https://github.com/jhausserlab/