Package: VAExprs Type: Package Title: Generating Samples of Gene Expression Data with Variational Autoencoders Description: 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. Version: 1.18.0 Date: 2022-05-16 Authors@R: c(person(given="Dongmin", family="Jung", email="dmdmjung@gmail.com", role=c("cre", "aut"), comment = c(ORCID = "0000-0001-7499-8422"))) Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 biocViews: Software, GeneExpression, SingleCell NeedsCompilation: no VignetteBuilder: knitr Config/pak/sysreqs: cmake libglpk-dev make default-jdk libicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev python3 libx11-dev zlib1g-dev Repository: Bioconductor 3.23 Date/Publication: 2026-04-28 12:56:36 UTC RemoteUrl: https://github.com/bioc/VAExprs RemoteRef: RELEASE_3_23 RemoteSha: 225150fc2060966171795cd9b6c742c7b94723a3 Packaged: 2026-07-04 05:53:53 UTC; root Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung