Package: HiLDA 1.27.0

Zhi Yang

HiLDA: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation

A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization.

Authors:Zhi Yang [aut, cre], Yuichi Shiraishi [ctb]

HiLDA_1.27.0.tar.gz
HiLDA_1.27.0.zip(r-4.7)HiLDA_1.27.0.zip(r-4.6)HiLDA_1.27.0.zip(r-4.5)
HiLDA_1.27.0.tgz(r-4.6-x86_64)HiLDA_1.27.0.tgz(r-4.6-arm64)HiLDA_1.27.0.tgz(r-4.5-x86_64)HiLDA_1.27.0.tgz(r-4.5-arm64)
HiLDA_1.27.0.tar.gz(r-4.7-arm64)HiLDA_1.27.0.tar.gz(r-4.7-x86_64)HiLDA_1.27.0.tar.gz(r-4.6-arm64)HiLDA_1.27.0.tar.gz(r-4.6-x86_64)
HiLDA_1.27.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
HiLDA/json (API)
NEWS

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

Bug tracker:https://github.com/uscbiostats/hilda/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.

On BioConductor:HiLDA-1.27.0(bioc 3.24)HiLDA-1.26.0(bioc 3.23)

softwaresomaticmutationsequencingstatisticalmethodbayesianmutational-signaturesrjagssomatic-mutationscppjags

5.56 score 3 stars 1 packages 7 scripts 370 downloads 2 mentions 13 exports 98 dependencies

Last updated from:9f1e36cc27. Checks:1 WARNING, 3 ERROR, 10 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING306
linux-devel-arm64ERROR316
linux-devel-x86_64ERROR325
source / vignettesOK396
linux-release-arm64OK365
linux-release-x86_64OK429
macos-release-arm64OK343
macos-release-x86_64OK629
macos-oldrel-arm64OK204
macos-oldrel-x86_64OK575
windows-develERROR347
windows-releaseOK340
windows-oldrelOK412
wasm-releaseOK235

Exports:hildaBarplothildaDiffPlothildaGlobalResulthildaLocalResulthildaPlotSignaturehildaReadMPFilehildaRhathildaTestpmBarplotpmgetSignaturepmMultiBarplotpmPlotSignaturevisPMS

Dependencies:abindAnnotationDbiaskpassBHBiobaseBiocBaseUtilsBiocGenericsBiocIOBiocParallelBiostringsbitbit64bitopsblobbootBSgenomeBSgenome.Hsapiens.UCSC.hg19cachemcigarilloclicodacodetoolscowplotcpp11crayoncurlDBIDelayedArraydplyrfarverfastmapforcatsformatRfutile.loggerfutile.optionsgenericsGenomicAlignmentsGenomicFeaturesGenomicRangesggplot2gluegtablehttrIRangesisobandjsonliteKEGGRESTlabelinglambda.rlatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpillarpkgconfigpngpurrrR2jagsR2WinBUGSR6RColorBrewerRcppRCurlrestfulrRhtslibrjagsrjsonrlangRsamtoolsRSQLitertracklayerS4ArraysS4VectorsS7scalesSeqinfosnowSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselectTxDb.Hsapiens.UCSC.hg19.knownGeneutf8vctrsviridisLitewithrXMLXVectoryaml

HiLDA: a package for testing the burdens of mutational signatures

Zhi Yang

Rendered fromHiLDA.Rmdusingknitr::rmarkdownon Jun 03 2026.

Last update: 2019-06-22
Started: 2019-06-22

Readme and manuals

Help Manual

Help pageTopics
Check whether the parameter F is within the appropriate rangeboundaryTurbo_F
Check whether the parameter Q is within the appropriate rangeboundaryTurbo_Q
A function for calculating the log-likelihood from the data and parameterscalcPMSLikelihood
Restore the converted parameter F for turboEMconvertFromTurbo_F
Restore the converted parameter Q for turboEMconvertFromTurbo_Q
Convert the parameter F so that turboEM can treatconvertToTurbo_F
Convert the parameter Q so that turboEM can treatconvertToTurbo_Q
An S4 class representing the estimated parametersEstimatedParameters-class
Calculate the value of the log-likelihood for given parametersgetLogLikelihoodC
Get mutation feature vector from context sequence data and reference and alternate allele informationgetMutationFeatureVector
Read the raw mutation data with the mutation feature vector format, estimate and plot both mutation signatures and their fractionshildaBarplot
Read the raw mutation data with the mutation feature vector format, estimate and plot both mutation signatures and their fractionshildaDiffPlot
Compute the Bayes factorhildaGlobalResult
Extract the posterior distributions of the mean differences in muational exposureshildaLocalResult
Plot mutation signatures from HiLDA outputhildaPlotSignature
Read the raw mutation data of Mutation Position Format.hildaReadMPFile
Output the maximum potential scale reduction statistic of all parameters estimatedhildaRhat
Apply HiLDA to statistically testing the global difference in burdens of mutation signatures between two groupshildaTest
An S4 class to represent a mutation meta information common to many data typesMetaInformation-class
An S4 class representing the mutation dataMutationFeatureData-class
A function for estimating parameters using Squared EM algorithmmySquareEM
Plot both mutation signatures and their mutational exposures from pmsignature outputpmBarplot
Obtain the parameters for mutation signatures and membershipspmgetSignature
Plot both mutation signatures and their mutational exposures from pmsignature output for more than two groupspmMultiBarplot
Plot mutation signatures from pmsignature outputpmPlotSignature
A functional for generating the function checking the parameter (p) is within the restricted conditions or notPMSboundary
Update the parameter F and Q (M-step in the EM-algorithm)updateMstepFQC
A function for updating parameters using EM-algorithmupdatePMSParam
Update the auxiliary parameters theta and normalize them so that the summation of each group sums to 1 (E-step), also calculate the current log-likelihood valueupdateTheta_NormalizedC
visualize probabisitic mutaiton signature for the independent modelvisPMS