Package: simpleSeg 1.9.0

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_1.9.0.tar.gz
simpleSeg_1.9.0.zip(r-4.5)simpleSeg_1.9.0.zip(r-4.4)simpleSeg_1.9.0.zip(r-4.3)
simpleSeg_1.9.0.tgz(r-4.4-any)simpleSeg_1.9.0.tgz(r-4.3-any)
simpleSeg_1.9.0.tar.gz(r-4.5-noble)simpleSeg_1.9.0.tar.gz(r-4.4-noble)
simpleSeg_1.9.0.tgz(r-4.4-emscripten)simpleSeg_1.9.0.tgz(r-4.3-emscripten)
simpleSeg.pdf |simpleSeg.html✨
simpleSeg/json (API)
NEWS

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

Peer review:

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

On BioConductor:simpleSeg-1.9.0(bioc 3.21)simpleSeg-1.8.0(bioc 3.20)

classificationsurvivalsinglecellnormalizationspatialspatial-statistics

5.12 score 1 stars 11 scripts 162 downloads 2 exports 142 dependencies

Last updated 23 days agofrom:4649541b86. Checks:OK: 4 WARNING: 3. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winWARNINGOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winWARNINGOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winWARNINGOct 31 2024
R-4.3-macOKOct 31 2024

Exports:normalizeCellssimpleSeg

Dependencies:abindaskpassbase64encbeeswarmBHBiobaseBiocFileCacheBiocGenericsBiocParallelbitbit64bitopsblobbslibcachemclicodetoolscolorspacecommonmarkcpp11crayoncurlcytomapperDBIdbplyrDelayedArraydeldirdigestdplyrEBImageevaluatefansifarverfastmapfftwtoolsfilelockfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggbeeswarmggplot2gluegridExtragtableHDF5ArrayhighrhtmltoolshtmlwidgetshttpuvhttrIRangesisobandjpegjquerylibjsonliteknitrlabelinglambda.rlaterlatticelifecyclelocfitmagickmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemunsellnlmennlsopensslpillarpkgconfigplogrpngpolyclippromisespurrrR6rappdirsrasterRColorBrewerRcppRCurlrhdf5rhdf5filtersRhdf5librjsonrlangrmarkdownRSQLiteS4ArraysS4VectorssassscalesshinyshinydashboardSingleCellExperimentsnowsourcetoolsspSparseArraySpatialExperimentspatstat.dataspatstat.geomspatstat.univarspatstat.utilsstringistringrSummarizedExperimentsvglitesvgPanZoomsyssystemfontsterratibbletidyrtidyselecttifftinytexUCSC.utilsutf8vctrsviporviridisviridisLitewithrxfunxtableXVectoryamlzlibbioc

Segmenting and normalizing multiplexed imaging data with simpleSeg

Alexander Nicholls, Ellis Patrick, Nicolas Canete

Rendered fromsimpleSeg.Rmdusingknitr::rmarkdownon Oct 31 2024.

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