Package: supersigs 1.13.0

Albert Kuo

supersigs: Supervised mutational signatures

Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).

Authors:Albert Kuo [aut, cre], Yifan Zhang [aut], Bahman Afsari [aut], Cristian Tomasetti [aut]

supersigs_1.13.0.tar.gz
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supersigs_1.13.0.tgz(r-4.4-any)supersigs_1.13.0.tgz(r-4.3-any)
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supersigs.pdf |supersigs.html
supersigs/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/tomasettilab/supersigs/issues

Datasets:

On BioConductor:supersigs-1.13.0(bioc 3.20)supersigs-1.12.0(bioc 3.19)

bioconductor-package

6 exports 0.61 score 107 dependencies

Last updated 2 months agofrom:cfee96f3e4

Exports:get_signaturemake_matrixpartial_signaturepredict_signatureprocess_vcfsimplify_signature

Dependencies:abindaskpassassertthatBiobaseBiocGenericsBiostringscaretclasscliclockcodetoolscolorspacecpp11crayoncurldata.tableDelayedArraydiagramdigestdplyre1071ellipsisfansifarverforeachfurrrfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2globalsgluegowergtablehardhathttripredIRangesisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimeModelMetricsmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartrsampleS4ArraysS4VectorsscalesshapesliderSparseArraySQUAREMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetzdbUCSC.utilsutf8vctrsviridisLitewarpwithrXVectorzlibbioc

Using supersigs

Rendered fromsupersigs.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2021-04-05
Started: 2020-02-03