Package: treeclimbR 1.8.0

Charlotte Soneson

treeclimbR: An algorithm to find optimal signal levels in a tree

The arrangement of hypotheses in a hierarchical structure appears in many research fields and often indicates different resolutions at which data can be viewed. This raises the question of which resolution level the signal should best be interpreted on. treeclimbR provides a flexible method to select optimal resolution levels (potentially different levels in different parts of the tree), rather than cutting the tree at an arbitrary level. treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one.

Authors:Ruizhu Huang [aut], Charlotte Soneson [aut, cre]

treeclimbR_1.8.0.tar.gz
treeclimbR_1.8.0.zip(r-4.7)treeclimbR_1.8.0.zip(r-4.6)treeclimbR_1.8.0.zip(r-4.5)
treeclimbR_1.8.0.tgz(r-4.6-any)treeclimbR_1.8.0.tgz(r-4.5-any)
treeclimbR_1.8.0.tar.gz(r-4.7-any)treeclimbR_1.8.0.tar.gz(r-4.6-any)
treeclimbR_1.8.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
treeclimbR/json (API)
NEWS

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

Bug tracker:https://github.com/csoneson/treeclimbr/issues

On BioConductor:treeclimbR-1.9.0(bioc 3.24)treeclimbR-1.8.0(bioc 3.23)

statisticalmethodcellbasedassays

6.52 score 20 stars 55 scripts 259 downloads 26 exports 184 dependencies

Last updated from:a9b35c31c0 (on RELEASE_3_23). Checks:1 NOTE, 9 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksNOTE250
linux-devel-x86_64OK490
source / vignettesOK347
linux-release-x86_64OK506
macos-release-arm64OK576
macos-oldrel-arm64OK243
windows-develOK373
windows-releaseOK400
windows-oldrelOK407
wasm-releaseOK172

Exports:aggDSbuildTreecalcMediansByTreeMarkercalcTreeCountscalcTreeMediansedgerWrpevalCandfdrfindChildfindExclgetCandgetDatagetLevelinfoCandisConnectmedianByClusterMarkernodeResultparEstimaterunDArunDSselNodesimDatatopNodestprTreeHeatmaptreeScore

Dependencies:abindALLapeaplotbackportsbase64encBHBiobaseBiocGenericsbiocmakeBiocParallelBiostringsbootbroombslibcachemcarcarDatacirclizecliclueclustercodetoolscolorRampscolorspaceComplexHeatmapConsensusClusterPluscorrplotcowplotcpp11crayoncytolibDelayedArrayDerivdiffcytdigestdir.expirydirmultdoBydoParalleldplyredgeRevaluatefarverfastmapfilelockflowCoreFlowSOMfontawesomefontBitstreamVerafontLiberationfontquiverforeachforecastformatRFormulafracdifffsfutile.loggerfutile.optionsgdtoolsgenericsGenomicRangesGetoptLongggforceggfunggiraphggnewscaleggplot2ggplotifyggpubrggrepelggsciggsignifggtreeGlobalOptionsgluegridExtragridGraphicsgtablehighrhtmltoolshtmlwidgetsigraphIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglambda.rlatticelazyevallifecyclelimmalme4lmtestlocfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompmvtnormnlmenloptrnnetnumDerivpatchworkpbkrtestpillarpkgconfigplyrpngpolyclippolynompurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2Rhdf5librjsonrlangrmarkdownRProtoBufLibrstatixRtsneS4ArraysS4VectorsS7sandwichsassscalesSeqinfoshapeSingleCellExperimentsnowSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsystemfontsTH.datatibbletidyrtidyselecttidytreetimeDatetinytextreeioTreeSummarizedExperimenttweenrurcautf8vctrsviridisviridisLitewithrxfunXMLXVectoryamlyulab.utilszoo

Finding optimal resolution of hierarchical hypotheses with treeclimbR

Charlotte Soneson and Ruizhu Huang

Rendered fromtreeclimbR.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2024-03-10
Started: 2024-02-10