A Framework For Multiplexed Image Processing And Spatial Analysis***
Author(s): Nils Eling, Jonas Windhager
Affiliation(s): University of Zurich, ETH Zurich
Twitter: @NilsEling
Simultaneous profiling of the spatial distributions of multiple biological molecules at single-cell resolution has recently been enabled by the development of highly multiplexed imaging technologies. Extracting and analyzing biologically relevant information contained in complex imaging data requires the use of a diverse set of computational tools and algorithms. We developed a standardized, user-friendly, customizable, and interoperable workflow for processing and analyzing data generated by highly multiplexed imaging technologies. The steinbock framework written in python supports image pre-processing, segmentation, feature extraction, and data export in a reproducible fashion. The imcRtools R/Bioconductor package forms the bridge between image processing and single-cell analysis by directly importing data generated by steinbock. The package further supports spatial data analysis such as patch detection, interaction testing and spatial clustering, and integrates with tools developed within the Bioconductor project. Image visualization and segmentation quality control is performed using the cytomapper R/Bioconductor package. Together, the tools described in this workflow facilitate the analysis of multiplexed imaging raw data at the single-cell and spatial level.
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