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Performing a Spatial Analysis of Multiplexed Tissue Imaging Data

Bioconductor version: Release (3.19)

We have developed an analytical framework for analysing data from high dimensional in situ cytometry assays including CODEX, CycIF, IMC and High Definition Spatial Transcriptomics. This framework makes use of functionality from our Bioconductor packages spicyR, lisaClust, scFeatures, FuseSOM, simpleSeg and ClassifyR and contains most of the key steps which are needed to interrogate the comprehensive spatial information generated by these exciting new technologies including cell segmentation, feature normalisation, cell type identification, micro-environment characterisation, spatial hypothesis testing and patient classification. Ultimately, our modular analysis framework provides a cohesive and accessible entry point into spatially resolved single cell data analysis for any R-based bioinformatician.

Author: Alexander Nicholls [aut], Nicholas Robertson [aut], Nicolas Canete [aut], Elijah Willie [aut], Ellis Patrick [aut] , SOMS Maintainer [aut, cre]

Maintainer: SOMS Maintainer <maths.bioconductor at>

Citation (from within R, enter citation("spicyWorkflow")):


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Introduction to a spicy workflow HTML R Script


biocViews ImmunoOncologyWorkflow, SpatialWorkflow, Workflow
Version 1.3.1
License GPL-3
Depends R (>= 4.3.0)
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Suggests knitr, rmarkdown, BiocStyle, EBImage, cytomapper, ggplot2, ggpubr, lisaClust, spicyR, ClassifyR, scater, dplyr, simpleSeg, FuseSOM, HDF5Array, parallel, tidySingleCellExperiment
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Source Package spicyWorkflow_1.3.1.tar.gz
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