Package: MCbiclust 1.31.0

Robert Bentham

MCbiclust: Massive correlating biclusters for gene expression data and associated methods

Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set.

Authors:Robert Bentham

MCbiclust_1.31.0.tar.gz
MCbiclust_1.31.0.zip(r-4.5)MCbiclust_1.31.0.zip(r-4.4)MCbiclust_1.31.0.zip(r-4.3)
MCbiclust_1.31.0.tgz(r-4.5-any)MCbiclust_1.31.0.tgz(r-4.4-any)MCbiclust_1.31.0.tgz(r-4.3-any)
MCbiclust_1.31.0.tar.gz(r-4.5-noble)MCbiclust_1.31.0.tar.gz(r-4.4-noble)
MCbiclust_1.31.0.tgz(r-4.4-emscripten)MCbiclust_1.31.0.tgz(r-4.3-emscripten)
MCbiclust.pdf |MCbiclust.html
MCbiclust/json (API)

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

On BioConductor:MCbiclust-1.31.0(bioc 3.21)MCbiclust-1.30.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologyclusteringmicroarraystatisticalmethodsoftwarernaseqgeneexpression

4.00 score 2 scripts 308 downloads 1 mentions 14 exports 124 dependencies

Last updated 5 months agofrom:990c62b8e3. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 29 2025
R-4.5-winOKMar 29 2025
R-4.5-macOKMar 29 2025
R-4.5-linuxOKMar 29 2025
R-4.4-winOKMar 29 2025
R-4.4-macOKMar 29 2025
R-4.4-linuxOKMar 29 2025
R-4.3-winOKMar 29 2025
R-4.3-macOKMar 29 2025

Exports:CorScoreCalcCVEvalCVPlotFindSeedForkClassifierGOEnrichmentAnalysisHclustGenesHiCorMultiSampleSortPrepPC1AlignPC1VecFunPointScoreCalcSampleSortSilhouetteClustGroupsThresholdBic

Dependencies:AnnotationDbiaskpassbackportsbase64encBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayoncurldata.tableDBIdigestdoParalleldplyrdynamicTreeCutevaluatefansifarverfastclusterfastmapfontawesomeforcatsforeachforeignformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGGallyggplot2ggstatsglueGO.dbgridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetshttrimputeIRangesisobanditeratorsjquerylibjsonliteKEGGRESTknitrlabelinglambda.rlatticelifecyclemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemunsellnlmennetopensslorg.Hs.eg.dbpatchworkpillarpkgconfigplogrplyrpngpreprocessCoreprettyunitsprogresspurrrR6rappdirsRColorBrewerRcpprlangrmarkdownrpartRSQLiterstudioapiS4VectorssassscalessnowstringistringrsurvivalsystibbletidyrtidyselecttinytexUCSC.utilsutf8vctrsviridisviridisLiteWGCNAwithrxfunXVectoryaml

Introduction to MCbiclust

Rendered fromMCbiclust_vignette.Rmdusingknitr::rmarkdownon Mar 29 2025.

Last update: 2024-02-11
Started: 2016-08-17