Package: BANDITS 1.23.0

Simone Tiberi

BANDITS: BANDITS: Bayesian ANalysis of DIfferenTial Splicing

BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts.

Authors:Simone Tiberi [aut, cre].

BANDITS_1.23.0.tar.gz
BANDITS_1.23.0.zip(r-4.5)BANDITS_1.23.0.zip(r-4.4)BANDITS_1.23.0.zip(r-4.3)
BANDITS_1.23.0.tgz(r-4.4-x86_64)BANDITS_1.23.0.tgz(r-4.4-arm64)BANDITS_1.23.0.tgz(r-4.3-x86_64)BANDITS_1.23.0.tgz(r-4.3-arm64)
BANDITS_1.23.0.tar.gz(r-4.5-noble)BANDITS_1.23.0.tar.gz(r-4.4-noble)
BANDITS_1.23.0.tgz(r-4.4-emscripten)BANDITS_1.23.0.tgz(r-4.3-emscripten)
BANDITS.pdf |BANDITS.html
BANDITS/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/simonetiberi/bandits/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • gene_tr_id - Gene-transcript matching
  • input_data - A 'BANDITS_data' object, generated with 'create_data'
  • precision - Estimates for the log-precision parameter
  • results - Results of the DTU test, generated with 'test_DTU'

On BioConductor:BANDITS-1.23.0(bioc 3.21)BANDITS-1.22.0(bioc 3.20)

differentialsplicingalternativesplicingbayesiangeneticsrnaseqsequencingdifferentialexpressiongeneexpressionmultiplecomparisonsoftwaretranscriptionstatisticalmethodvisualizationopenblascpp

5.75 score 17 stars 1 packages 11 scripts 338 downloads 1 mentions 14 exports 75 dependencies

Last updated 2 months agofrom:46f9bab9ce. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-win-x86_64NOTENov 29 2024
R-4.5-linux-x86_64NOTENov 29 2024
R-4.4-win-x86_64NOTENov 29 2024
R-4.4-mac-x86_64NOTENov 29 2024
R-4.4-mac-aarch64NOTENov 29 2024
R-4.3-win-x86_64NOTENov 29 2024
R-4.3-mac-x86_64NOTENov 29 2024
R-4.3-mac-aarch64NOTENov 29 2024

Exports:convergencecreate_dataeff_len_computefilter_genesfilter_transcriptsgeneplot_precisionplot_proportionsprior_precisionshowtest_DTUtop_genestop_transcriptstranscript

Dependencies:askpassBHBiocGenericsBiocParallelclicodetoolscolorspacecpp11curldata.tabledigestdoParalleldoRNGDRIMSeqedgeRfansifarverforeachformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehttrIRangesisobanditeratorsjsonlitelabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixmgcvmimemunsellnlmeopensslpillarpkgconfigplyrR.methodsS3R.ooR.utilsR6RColorBrewerRcppRcppArmadilloreshape2rlangrngtoolsS4VectorsscalessnowstatmodstringistringrsystibbleUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

BANDITS: Bayesian ANalysis of DIfferenTial Splicing

Rendered fromBANDITS.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2023-11-13
Started: 2019-04-01

Readme and manuals

Help Manual

Help pageTopics
BANDITS: Bayesian ANalysis of DIfferenTial SplicingBANDITS-package BANDITS
BANDITS_data classBANDITS_data BANDITS_data-class show,BANDITS_data-method
BANDITS_test classBANDITS_test BANDITS_test-class convergence convergence,BANDITS_test-method gene gene,BANDITS_test-method plot_proportions plot_proportions,BANDITS_test-method show,BANDITS_test-method top_genes top_genes,BANDITS_test-method top_transcripts top_transcripts,BANDITS_test-method transcript transcript,BANDITS_test-method
Create a 'BANDITS_data' objectcreate_data
Compute the median effective length of transcripts.eff_len_compute
Filter lowly abundant genes.filter_genes
Filter lowly abundant transcripts.filter_transcripts
Gene-transcript matchinggene_tr_id
A 'BANDITS_data' object, generated with 'create_data'input_data
Plot the log-precision estimatesplot_precision
Estimates for the log-precision parameter, generated with 'prior_precision'precision
Infer an informative prior for the precisionprior_precision
Results of the DTU test, generated with 'test_DTU'results
Perform differential splicingtest_DTU