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GSR: Simulator - cscGAN
| Attribute | Value |
|---|---|
| Title | cscGAN |
| Short Description | cscGAN learns non-linear gene-gene dependencies from cell type samples in order to generate realistic cells of defined types. |
| Long Description | cscGAN learns non-linear gene-gene dependencies from cell type samples in order to generate realistic cells of definitely types. Augmenting sparse cell populations improves the detection of marker genes, the robustness of and reliability of classifiers as well as the assessment of novel analysis algorithms. |
| Keywords | machine-learning, Generative Adversarial Neural Network, |
| Project Started | 2018 |
| Last Release | 6 years, 11 months ago |
| Homepage | https://github.com/imsb-uke/scGAN |
| Citations | Marouf M, Machart P, Bansal V, Kilian C, Magruder DS, Krebs CF, Bonn S, Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks., Nat Commun, Jan. 9, 2020 [ Abstract, cited in PMC ] |
| GSR Certification |  ✔ Accessibility |
| Last evaluated | Sept. 30, 2022 (1056 days ago) |
| Author verification | The basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator. |
| Attribute Category | Attribute |
|---|---|
| Target | |
| Type of Simulated Data | RNA, Single-Cell Sequencing, |
| Variations | |
| Simulation Method | Machine Learning, |
| Input | |
| Data Type | |
| File format | |
| Output | |
| Data Type | Gene Expression, |
| Sequencing Reads | |
| File Format | |
| Sample Type | |
| Phenotype | |
| Trait Type | |
| Determinants | |
| Evolutionary Features | |
| Demographic | |
| Population Size Changes | |
| Gene Flow | |
| Spatiality | |
| Life Cycle | |
| Mating System | |
| Fecundity | |
| Natural Selection | |
| Determinant | |
| Models | |
| Recombination | |
| Mutation Models | |
| Events Allowed | |
| Other | |
| Interface | |
| Development | |
| Tested Platforms | |
| Language | Python, Other, |
| License | MIT, |
| GSR Certification | Accessibility, Documentation, Application, Support, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 2
The following 2 publications are selected examples of applications that used cscGAN.
2023
Liu C, Huang H, Yang P, Multi-task learning from multimodal single-cell omics with Matilda., Nucleic Acids Res, May 8, 2023 [Abstract]
2022
Heydari AA, Davalos OA, Zhao L, Hoyer KK, Sindi SS, ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders., Bioinformatics, April 12, 2022 [Abstract]