Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering.

ABSTRACT: While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can

SUBMITTER: Liu CC 

PROVIDER: S-EPMC10393943 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature