Package: treeclimbR 1.3.0

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_1.3.0.tar.gz
treeclimbR_1.3.0.zip(r-4.5)treeclimbR_1.3.0.zip(r-4.4)
treeclimbR_1.1.2.tgz(r-4.4-any)
treeclimbR_1.3.0.tar.gz(r-4.5-noble)treeclimbR_1.3.0.tar.gz(r-4.4-noble)
treeclimbR_1.3.0.tgz(r-4.4-emscripten)
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'))

Peer review:

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

On BioConductor:treeclimbR-1.3.0(bioc 3.21)treeclimbR-1.2.0(bioc 3.20)

statisticalmethodcellbasedassays

7.11 score 19 stars 45 scripts 164 downloads 26 exports 158 dependencies

Last updated 23 days agofrom:653ec0b190. Checks:OK: 2 NOTE: 1 WARNING: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winWARNINGOct 31 2024
R-4.5-linuxNOTEOct 31 2024
R-4.4-winWARNINGOct 31 2024
R-4.4-macOKOct 12 2024

Exports:aggDSbuildTreecalcMediansByTreeMarkercalcTreeCountscalcTreeMediansedgerWrpevalCandfdrfindChildfindExclgetCandgetDatagetLevelinfoCandisConnectmedianByClusterMarkernodeResultparEstimaterunDArunDSselNodesimDatatopNodestprTreeHeatmaptreeScore

Dependencies:abindALLapeaplotaskpassbackportsBHBiobaseBiocGenericsBiocParallelBiostringsbootbroomcarcarDatacirclizecliclueclustercodetoolscolorRampscolorspaceComplexHeatmapConsensusClusterPluscorrplotcowplotcpp11crayoncurlcytolibDelayedArrayDerivdiffcytdigestdirmultdoBydoParalleldplyredgeRfansifarverflowCoreFlowSOMforeachformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggforceggfunggnewscaleggplot2ggplotifyggpubrggrepelggsciggsignifggtreeGlobalOptionsgluegridExtragridGraphicsgtablehttrigraphIRangesisobanditeratorsjsonlitelabelinglambda.rlatticelazyevallifecyclelimmalme4locfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmgcvmicrobenchmarkmimeminqamodelrmultcompmunsellmvtnormnlmenloptrnnetnumDerivopensslpatchworkpbkrtestpillarpkgconfigplyrpngpolyclippolynompurrrquantregR6RColorBrewerRcppRcppEigenreshape2Rhdf5librjsonrlangRProtoBufLibrstatixRtsneS4ArraysS4VectorssandwichscalesshapeSingleCellExperimentsnowSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsyssystemfontsTH.datatibbletidyrtidyselecttidytreetreeioTreeSummarizedExperimenttweenrUCSC.utilsutf8vctrsviridisviridisLitewithrXMLXVectoryulab.utilszlibbioczoo

Finding optimal resolution of hierarchical hypotheses with treeclimbR

Charlotte Soneson and Ruizhu Huang

Rendered fromtreeclimbR.Rmdusingknitr::rmarkdownon Oct 31 2024.

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