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VAExprs

Generating Samples of Gene Expression Data with Variational Autoencoders


Bioconductor version: Release (3.18)

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 "4.3") 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")
VAExprs HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews GeneExpression, SingleCell, Software
Version 1.8.0
In Bioconductor since BioC 3.14 (R-4.1) (2.5 years)
License Artistic-2.0
Depends keras, mclust
Imports SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats
System Requirements
URL
See More
Suggests SC3, knitr, testthat, reticulate, rmarkdown
Linking To
Enhances
Depends On Me
Imports Me
Suggests Me GenProSeq
Links To Me
Build Report Build Report

Package Archives

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

Source Package VAExprs_1.8.0.tar.gz
Windows Binary VAExprs_1.8.0.zip (64-bit only)
macOS Binary (x86_64) VAExprs_1.8.0.tgz
macOS Binary (arm64) VAExprs_1.8.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/VAExprs
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/VAExprs
Bioc Package Browser https://code.bioconductor.org/browse/VAExprs/
Package Short Url https://bioconductor.org/packages/VAExprs/
Package Downloads Report Download Stats