Package: treeclimbR 1.3.1

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_1.3.1.tar.gz
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treeclimbR.pdf |treeclimbR.html✨
treeclimbR/json (API)
NEWS

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

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

On BioConductor:treeclimbR-1.3.1(bioc 3.21)treeclimbR-1.2.0(bioc 3.20)

statisticalmethodcellbasedassays

7.13 score 20 stars 45 scripts 153 downloads 26 exports 160 dependencies

Last updated 2 months agofrom:e00df7c012. Checks:5 OK, 1 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 31 2025
R-4.5-winOKJan 31 2025
R-4.5-macWARNINGJan 31 2025
R-4.5-linuxOKJan 31 2025
R-4.4-winOKJan 31 2025
R-4.4-macOKJan 31 2025

Exports:aggDSbuildTreecalcMediansByTreeMarkercalcTreeCountscalcTreeMediansedgerWrpevalCandfdrfindChildfindExclgetCandgetDatagetLevelinfoCandisConnectmedianByClusterMarkernodeResultparEstimaterunDArunDSselNodesimDatatopNodestprTreeHeatmaptreeScore

Dependencies:abindALLapeaplotaskpassbackportsBHBiobaseBiocGenericsBiocParallelBiostringsbootbroomcarcarDatacirclizecliclueclustercodetoolscolorRampscolorspaceComplexHeatmapConsensusClusterPluscorrplotcowplotcpp11crayoncurlcytolibDelayedArrayDerivdiffcytdigestdirmultdoBydoParalleldplyredgeRfansifarverflowCoreFlowSOMforeachformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggforceggfunggnewscaleggplot2ggplotifyggpubrggrepelggsciggsignifggtreeGlobalOptionsgluegridExtragridGraphicsgtablehttrigraphIRangesisobanditeratorsjsonlitelabelinglambda.rlatticelazyevallifecyclelimmalme4locfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmgcvmicrobenchmarkmimeminqamodelrmultcompmunsellmvtnormnlmenloptrnnetnumDerivopensslpatchworkpbkrtestpillarpkgconfigplyrpngpolyclippolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasreshape2Rhdf5librjsonrlangRProtoBufLibrstatixRtsneS4ArraysS4VectorssandwichscalesshapeSingleCellExperimentsnowSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsyssystemfontsTH.datatibbletidyrtidyselecttidytreetreeioTreeSummarizedExperimenttweenrUCSC.utilsutf8vctrsviridisviridisLitewithrXMLXVectoryulab.utilszoo

Finding optimal resolution of hierarchical hypotheses with treeclimbR

Charlotte Soneson and Ruizhu Huang

Rendered fromtreeclimbR.Rmdusingknitr::rmarkdownon Jan 31 2025.

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