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✨
DESCRIPTION |NEWS
card.svg |card.png
HiLDA/json (API)

# 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

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

software somaticmutation sequencing statisticalmethod bayesian mutational-signatures rjags somatic-mutations cpp jags

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

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

Target	Result	Time
bioc-checks	WARNING	269
linux-devel-arm64	ERROR	305
linux-devel-x86_64	ERROR	374
source / vignettes	OK	446
linux-release-arm64	OK	366
linux-release-x86_64	OK	431
macos-release-arm64	OK	385
macos-release-x86_64	OK	481
macos-oldrel-arm64	OK	255
macos-oldrel-x86_64	OK	425
windows-devel	ERROR	282
windows-release	OK	438
windows-oldrel	OK	376
wasm-release	OK	261

Exports:hildaBarplot hildaDiffPlot hildaGlobalResult hildaLocalResult hildaPlotSignature hildaReadMPFile hildaRhat hildaTest pmBarplot pmgetSignature pmMultiBarplot pmPlotSignature visPMS

Dependencies:abind AnnotationDbi askpass BH Biobase BiocBaseUtils BiocGenerics BiocIO BiocParallel Biostrings bit bit64 bitops blob boot BSgenome BSgenome.Hsapiens.UCSC.hg19 cachem cigarillo cli coda codetools cowplot cpp11 crayon curl DBI DelayedArray dplyr farver fastmap forcats formatR futile.logger futile.options generics GenomicAlignments GenomicFeatures GenomicRanges ggplot2 glue gtable httr IRanges isoband jsonlite KEGGREST labeling lambda.r lattice lifecycle magrittr Matrix MatrixGenerics matrixStats memoise mime openssl pillar pkgconfig png purrr R2jags R2WinBUGS R6 RColorBrewer Rcpp RCurl restfulr Rhtslib rjags rjson rlang Rsamtools RSQLite rtracklayer S4Arrays S4Vectors S7 scales Seqinfo snow SparseArray stringi stringr SummarizedExperiment sys tibble tidyr tidyselect TxDb.Hsapiens.UCSC.hg19.knownGene utf8 vctrs viridisLite withr XML XVector yaml

HiLDA: a package for testing the burdens of mutational signatures

Rendered fromHiLDA.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Check whether the parameter F is within the appropriate range	boundaryTurbo_F
Check whether the parameter Q is within the appropriate range	boundaryTurbo_Q
A function for calculating the log-likelihood from the data and parameters	calcPMSLikelihood
Restore the converted parameter F for turboEM	convertFromTurbo_F
Restore the converted parameter Q for turboEM	convertFromTurbo_Q
Convert the parameter F so that turboEM can treat	convertToTurbo_F
Convert the parameter Q so that turboEM can treat	convertToTurbo_Q
An S4 class representing the estimated parameters	EstimatedParameters-class
Calculate the value of the log-likelihood for given parameters	getLogLikelihoodC
Get mutation feature vector from context sequence data and reference and alternate allele information	getMutationFeatureVector
Read the raw mutation data with the mutation feature vector format, estimate and plot both mutation signatures and their fractions	hildaBarplot
Read the raw mutation data with the mutation feature vector format, estimate and plot both mutation signatures and their fractions	hildaDiffPlot
Compute the Bayes factor	hildaGlobalResult
Extract the posterior distributions of the mean differences in muational exposures	hildaLocalResult
Plot mutation signatures from HiLDA output	hildaPlotSignature
Read the raw mutation data of Mutation Position Format.	hildaReadMPFile
Output the maximum potential scale reduction statistic of all parameters estimated	hildaRhat
Apply HiLDA to statistically testing the global difference in burdens of mutation signatures between two groups	hildaTest
An S4 class to represent a mutation meta information common to many data types	MetaInformation-class
An S4 class representing the mutation data	MutationFeatureData-class
A function for estimating parameters using Squared EM algorithm	mySquareEM
Plot both mutation signatures and their mutational exposures from pmsignature output	pmBarplot
Obtain the parameters for mutation signatures and memberships	pmgetSignature
Plot both mutation signatures and their mutational exposures from pmsignature output for more than two groups	pmMultiBarplot
Plot mutation signatures from pmsignature output	pmPlotSignature
A functional for generating the function checking the parameter (p) is within the restricted conditions or not	PMSboundary
Update the parameter F and Q (M-step in the EM-algorithm)	updateMstepFQC
A function for updating parameters using EM-algorithm	updatePMSParam
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 value	updateTheta_NormalizedC
visualize probabisitic mutaiton signature for the independent model	visPMS