Package: simpleSeg 1.14.0

Ellis Patrick

simpleSeg: A package to perform simple cell segmentation

Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images.

Authors:Nicolas Canete [aut], Alexander Nicholls [aut], Ellis Patrick [aut, cre]

simpleSeg_1.14.0.tar.gz
simpleSeg_1.14.0.zip(r-4.7)simpleSeg_1.14.0.zip(r-4.6)simpleSeg_1.14.0.zip(r-4.5)
simpleSeg_1.14.0.tgz(r-4.6-any)simpleSeg_1.14.0.tgz(r-4.5-any)
simpleSeg_1.14.0.tar.gz(r-4.7-any)simpleSeg_1.14.0.tar.gz(r-4.6-any)
simpleSeg_1.14.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
simpleSeg/json (API)
NEWS

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

Bug tracker:https://github.com/sydneybiox/simpleseg/issues

Pkgdown/docs site:https://sydneybiox.github.io

On BioConductor:simpleSeg-1.15.0(bioc 3.24)simpleSeg-1.14.0(bioc 3.23)

classificationsurvivalsinglecellnormalizationspatialspatial-statistics

6.05 score 1 stars 2 packages 25 scripts 473 downloads 2 exports 137 dependencies

Last updated from:7a20cd63e9 (on RELEASE_3_23). Checks:1 WARNING, 9 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksWARNING208
linux-devel-x86_64OK535
source / vignettesOK407
linux-release-x86_64OK536
macos-release-arm64OK324
macos-oldrel-arm64OK436
windows-develOK561
windows-releaseOK459
windows-oldrelOK464
wasm-releaseOK211

Exports:normalizeCellssimpleSeg

Dependencies:abindaskpassbase64encbeeswarmBHBiobaseBiocFileCacheBiocGenericsbiocmakeBiocParallelbitbit64bitopsblobbslibcachemclicodetoolscommonmarkcpp11curlcytomapperDBIdbplyrDelayedArraydeldirdigestdir.expirydplyrEBImageevaluatefarverfastmapfftwtoolsfilelockfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenomicRangesggbeeswarmggplot2gluegridExtragtableh5mreadHDF5Arrayhighrhtmltoolshtmlwidgetshttpuvhttr2IRangesisobandjpegjquerylibjsonliteknitrlabelinglambda.rlaterlatticelifecyclelocfitmagickmagrittrMatrixMatrixGenericsmatrixStatsmemoisemimennlsopensslotelpillarpkgconfigpngpolyclippromisespurrrR6rappdirsrasterRColorBrewerRcppRCurlrhdf5rhdf5filtersRhdf5librjsonrlangrmarkdownRSQLiteS4ArraysS4VectorsS7sassscalesSeqinfoshinyshinydashboardSingleCellExperimentsnowsourcetoolsspSparseArraySpatialExperimentspatstat.dataspatstat.geomspatstat.univarspatstat.utilsstringistringrSummarizedExperimentsvglitesvgPanZoomsyssystemfontsterratextshapingtibbletidyrtidyselecttifftinytexutf8vctrsviporviridisviridisLitewithrxfunxtableXVectoryaml

Segmenting and normalizing multiplexed imaging data with simpleSeg

Alexander Nicholls, Ellis Patrick, Nicolas Canete

Rendered fromsimpleSeg.Rmdusingknitr::rmarkdownon Jun 12 2026.

Last update: 2024-05-21
Started: 2022-05-30