VAExprs

DOI: 10.18129/B9.bioc.VAExprs    

Generating Samples of Gene Expression Data with Variational Autoencoders

Bioconductor version: Release (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>

Installation

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

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

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")

 

HTML	R Script	VAExprs
PDF		Reference Manual
Text		NEWS

Details

biocViews	GeneExpression, SingleCell, Software
Version	1.2.1
In Bioconductor since	BioC 3.14 (R-4.1) (1 year)
License	Artistic-2.0
Depends	keras, mclust
Imports	SingleCellExperiment, SummarizedExperiment, tensorflow, scater, 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_1.2.1.tar.gz
Windows Binary	VAExprs_1.2.1.zip
macOS Binary (x86_64)	VAExprs_1.2.1.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
Old Source Packages for BioC 3.15	Source Archive