Package: cytomapper 1.25.0

Lasse Meyer

cytomapper: Visualization of highly multiplexed imaging data in R

Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.

Authors:Nils Eling [aut], Nicolas Damond [aut], Tobias Hoch [ctb], Lasse Meyer [cre, ctb]

cytomapper_1.25.0.tar.gz
cytomapper_1.25.0.zip(r-4.7)cytomapper_1.25.0.zip(r-4.6)cytomapper_1.25.0.zip(r-4.5)
cytomapper_1.25.0.tgz(r-4.6-any)cytomapper_1.25.0.tgz(r-4.5-any)
cytomapper_1.25.0.tar.gz(r-4.7-any)cytomapper_1.25.0.tar.gz(r-4.6-any)
cytomapper_1.25.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
cytomapper/json (API)
NEWS

# Install 'cytomapper' in R:
install.packages('cytomapper', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bodenmillergroup/cytomapper/issues

Datasets:

On BioConductor:cytomapper-1.25.0(bioc 3.24)cytomapper-1.24.0(bioc 3.23)

immunooncologysoftwaresinglecellonechanneltwochannelmultiplecomparisonnormalizationdataimportbioimagingimaging-mass-cytometrysingle-cellspatial-analysis

9.79 score 36 stars 5 packages 478 scripts 859 downloads 1 mentions 18 exports 130 dependencies

Last updated from:7f3973c8cc. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING243
linux-devel-x86_64NOTE918
source / vignettesOK374
linux-release-x86_64NOTE828
macos-release-arm64NOTE441
macos-oldrel-arm64NOTE423
windows-develNOTE1088
windows-releaseNOTE1152
windows-oldrelNOTE1125
wasm-releaseOK203

Exports:channelNameschannelNames<-coercecompImageCytoImageListcytomapperShinygetChannelsgetImagesloadImagesmeasureObjectsmergeChannelsnormalizeplotCellsplotPixelsscaleImagessetChannels<-setImages<-show

Dependencies:abindaskpassbase64encbeeswarmBHBiobaseBiocFileCacheBiocGenericsbiocmakeBiocParallelbitbit64bitopsblobbslibcachemclicodetoolscommonmarkcpp11curlDBIdbplyrDelayedArraydigestdir.expirydplyrEBImageevaluatefarverfastmapfftwtoolsfilelockfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenomicRangesggbeeswarmggplot2gluegridExtragtableh5mreadHDF5Arrayhighrhtmltoolshtmlwidgetshttpuvhttr2IRangesisobandjpegjquerylibjsonliteknitrlabelinglambda.rlaterlatticelifecyclelocfitmagickmagrittrMatrixMatrixGenericsmatrixStatsmemoisemimennlsopensslotelpillarpkgconfigpngpromisespurrrR6rappdirsrasterRColorBrewerRcppRCurlrhdf5rhdf5filtersRhdf5librjsonrlangrmarkdownRSQLiteS4ArraysS4VectorsS7sassscalesSeqinfoshinyshinydashboardSingleCellExperimentsnowsourcetoolsspSparseArraySpatialExperimentstringistringrSummarizedExperimentsvglitesvgPanZoomsyssystemfontsterratextshapingtibbletidyrtidyselecttifftinytexutf8vctrsviporviridisviridisLitewithrxfunxtableXVectoryaml

On disk storage and handling of images

Rendered fromcytomapper_ondisk.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2021-04-23
Started: 2021-03-20

Visualization of imaging cytometry data in R

Rendered fromcytomapper.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2021-09-15
Started: 2020-05-07

Readme and manuals

Help Manual

Help pageTopics
Performs channel compensation on multi-channel imagescompImage
S4 class for list of imagescoerce,ANY,CytoImageList-method coerce,list,CytoImageList-method CytoImageList CytoImageList-class show,CytoImageList-method
Manipulating CytoImageList objectsCytoImageList-manipulation normalize normalize,CytoImageList-method scaleImages scaleImages,CytoImageList-method
Getting and setting the channel and image nameschannelNames channelNames,CytoImageList-method channelNames<- channelNames<-,CytoImageList-method CytoImageList-naming names,CytoImageList-method names<-,CytoImageList-method
General subsetting methods for CytoImageList objectsCytoImageList-subsetting getChannels getChannels,CytoImageList-method getImages getImages,CytoImageList-method mergeChannels setChannels<- setChannels<-,CytoImageList-method setImages<- setImages<-,CytoImageList-method [<-,CytoImageList,ANY,ANY,CytoImageList-method [[<-,CytoImageList,ANY,ANY-method
Shiny application to visualise gated cells on imagescytomapperShiny
Read images into CytoImageList objectloadImages
Compute morphological and intensity features from objects on images.measureObjects
Example CytoImageList object of image filespancreasImages
Example CytoImageList object of segmentation maskspancreasMasks
Example SingleCellExperiment objectpancreasSCE
Function to visualize cell-level information on segmentation masksplotCells
Function to visualize pixel-level information of multi-channel imagesplotPixels
Further plotting parameters for the cytomapper packageplotting-param