Package: cytomapper 1.25.1

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_1.25.1.tar.gz
cytomapper_1.25.1.zip(r-4.7-any)cytomapper_1.25.1.zip(r-4.6-any)cytomapper_1.25.1.zip(r-4.5-any)
cytomapper_1.25.1.tgz(r-4.6-any)cytomapper_1.25.1.tgz(r-4.5-any)
cytomapper_1.25.1.tar.gz(r-4.7-any)cytomapper_1.25.1.tar.gz(r-4.6-any)
cytomapper_1.25.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cytomapper/json (API)

# 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-1.25.1(bioc 3.24)cytomapper-1.24.0(bioc 3.23)

immunooncologysoftwaresinglecellonechanneltwochannelmultiplecomparisonnormalizationdataimportbioimagingimaging-mass-cytometrysingle-cellspatial-analysis

9.94 score 37 stars 5 packages 540 scripts 1.0k downloads 1 mentions 18 exports 130 dependencies

Last updated from:2efbbe1b58. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksERROR194
linux-develNOTE891
source / vignettesOK374
linux-releaseNOTE760
macos-releaseNOTE744
macos-oldrelNOTE742
windows-develNOTE1090
windows-releaseNOTE1109
windows-oldrelNOTE1104
wasm-releaseOK172

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

Dependencies:abindaskpassbase64encbeeswarmBHBiobaseBiocFileCacheBiocGenericsbiocmakeBiocParallelbitbit64bitopsblobbslibcachemclicodetoolscommonmarkcpp11curlDBIdbplyrDelayedArraydigestdir.expirydplyrEBImageevaluatefarverfastmapfftwtoolsfilelockfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenomicRangesggbeeswarmggplot2gluegridExtragtableh5mreadHDF5Arrayhighrhtmltoolshtmlwidgetshttpuvhttr2IRangesisobandjpegjquerylibjsonliteknitrlabelinglambda.rlaterlatticelifecyclelocfitmagickmagrittrMatrixMatrixGenericsmatrixStatsmemoisemimennlsopensslotelpillarpkgconfigpngpromisespurrrR6rappdirsrasterRColorBrewerRcppRCurlrhdf5rhdf5filtersRhdf5librjsonrlangrmarkdownRSQLiteS4ArraysS4VectorsS7sassscalesSeqinfoshinyshinydashboardSingleCellExperimentsnowsourcetoolsspSparseArraySpatialExperimentstringistringrSummarizedExperimentsvglitesvgPanZoomsyssystemfontsterratextshapingtibbletidyrtidyselecttifftinytexutf8vctrsviporviridisviridisLitewithrxfunxtableXVectoryaml

Visualization of imaging cytometry data in R
Introduction | Quick start | Data formats | The provided toy dataset | Reading in data | Load images | Add metadata | Scale images | Add channel names | Generating the SingleCellExperiment object | The CytoImageList object | Accessors | Getting and setting images | Getting and setting channels | Naming and merging channels | Looping | Plotting pixel information | Normalization | Colouring | Adjusting brightness, contrast and gamma | Outlining | Subsetting | Adjusting the colour | Plotting cell information | Changing the assay slot | Outlining | Subsetting | Adjusting the colour | Customisation | Subsetting the SingleCellExperiment object | Background and missing colour | Scale bar and image title | Legend | Setting the margin between images | Scale the feature counts | Image interpolation | Thick borders | Returning plots and images | Integration with ggplot2 objects | Saving images | Gating cells on images | Acknowledgements | Contributions | Session info | References
Rendered fromcytomapper.Rmdusingknitr::rmarkdown

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

On disk storage and handling of images
Introduction | Reading in data to disk | Converting from on disk to memory and back | Effects on package functionality | Session info | References
Rendered fromcytomapper_ondisk.Rmdusingknitr::rmarkdown

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

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