VAExprs
Bioc currentGenerating Samples of Gene Expression Data with Variational Autoencoders
Release Lineage
Entered 3.14 · Oct 27, 2021
Current · Requires R 4.6
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.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
7 4 exported
Complexity
7.9 avg / 37 max
Call network
7 nodes / 3 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
1,401
Files
16
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
4
Internal functions
3
Testing & CI
Has tests
Yes
Test-to-code ratio
0.19
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
12
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
–
System requirements
–
C++ standard
–
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
10
First release
2021-12-15
Latest release
2026-04-28
Avg cadence
181 days
Cold removal rate
–
Dep drift
1
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 100%
- Documented parameters
- 74%
- Return-value docs
- 100%
- References docs
- 25%
Topics
Depended on by (1)
Bioconductor (1)
People
- Dongmin Jung maintainer author