VAExprs

DOI: 10.18129/B9.bioc.VAExprs    

This is the development version of VAExprs; for the stable release version, see VAExprs.

Generating Samples of Gene Expression Data with Variational Autoencoders

Bioconductor version: Development (3.15)

A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones.

Author: Dongmin Jung [cre, aut]

Maintainer: Dongmin Jung <dmdmjung at gmail.com>

Citation (from within R, enter citation("VAExprs")):

Installation

To install this package, start R (version "4.2") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("VAExprs")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("VAExprs")

 

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Details

biocViews GeneExpression, SingleCell, Software
Version 1.1.0
In Bioconductor since BioC 3.14 (R-4.1) (< 6 months)
License Artistic-2.0
Depends keras, mclust
Imports SingleCellExperiment, SummarizedExperiment, tensorflow, scater, gradDescent, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats
LinkingTo
Suggests SC3, knitr, testthat, reticulate, rmarkdown
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package VAExprs_1.1.0.tar.gz
Windows Binary
macOS 10.13 (High Sierra) VAExprs_1.1.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/VAExprs
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/VAExprs
Package Short Url https://bioconductor.org/packages/VAExprs/
Package Downloads Report Download Stats

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