Package: NetActivity 1.7.0

Carlos Ruiz-Arenas

NetActivity: Compute gene set scores from a deep learning framework

#' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models.

Authors:Carlos Ruiz-Arenas [aut, cre]

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On BioConductor:NetActivity-1.7.0(bioc 3.20)NetActivity-1.6.0(bioc 3.19)

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bioconductor-package

2 exports 0.49 score 70 dependencies

Last updated 2 months agofrom:a57b155893

Exports:computeGeneSetScoresprepareSummarizedExperiment

Dependencies:abindairwayaskpassBHBiobaseBiocGenericsBiocParallelclicodetoolscolorspacecpp11crayoncurlDelayedArrayDelayedMatrixStatsDESeq2fansifarverformatRfutile.loggerfutile.optionsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehttrIRangesisobandjsonlitelabelinglambda.rlatticelifecyclelocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellNetActivityDatanlmeopensslpillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangS4ArraysS4VectorsscalessnowSparseArraysparseMatrixStatsSummarizedExperimentsystibbleUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc