bioc-issue-bot
commented
3 years ago Hi @dongminjung
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: VAExprs
Type: Package
Title: Genearting Samples with 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: 0.99.0
Date: 2021-06-07
Authors@R: c(person(given="Dongmin", family="Jung", email="dmdmjung@gmail.com", role=c("cre", "aut"), comment = c(ORCID = "0000-0001-7499-8422")))
LazyData: TRUE
Depends: keras
Imports: SingleCellExperiment, SummarizedExperiment, scater, gradDescent, CatEncoders, DeepPINCS, mclust, purrr, DiagrammeR, stats
Suggests: SC3, knitr, testthat, reticulate, rmarkdown
SystemRequirement: tensorflow
License: Artistic-2.0
biocViews: Software, GeneExpression, SingleCell
NeedsCompilation: no
VignetteBuilder: knitr
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