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cff-version: 1.2.0
message: 'To cite package "VAExprs" in publications use:'
type: software
license: Artistic-2.0
title: 'VAExprs: Generating Samples of Gene Expression Data with Variational Autoencoders'
version: 1.11.0
abstract: 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.
authors:
- family-names: Jung
  given-names: Dongmin
  email: dmdmjung@gmail.com
  orcid: https://orcid.org/0000-0001-7499-8422
repository: https://bioc.r-universe.dev
date-released: '2022-05-16'
contact:
- family-names: Jung
  given-names: Dongmin
  email: dmdmjung@gmail.com
  orcid: https://orcid.org/0000-0001-7499-8422