NetActivity
Compute gene set scores from a deep learning framework
Bioconductor version: 3.23 · Package version: 1.14.0
#' 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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("NetActivity") Details
| Maintainer | Carlos Ruiz-Arenas <carlos.ruiza@upf.edu> |
| Author | Carlos Ruiz-Arenas [aut, cre] |
| License | MIT + file LICENSE |
| Downloads rank | 229 |
| Source branch | RELEASE_3_23 |
| biocViews | FunctionalGenomics, GO, GeneExpression, Microarray, Pathways, RNASeq, Software, Transcription |
Documentation
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Dependencies
Depends: R (>= 4.1.0)
Imports: airway, DelayedArray, DelayedMatrixStats, DESeq2, methods, methods, NetActivityData, SummarizedExperiment, utils
Suggests: AnnotationDbi, BiocStyle, Fletcher2013a, knitr, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0), tidyverse