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| 001 | 145037 | ||
| 005 | 20240423115938.0 | ||
| 024 | 7 | _ | |a 10.1038/s41467-019-14018-z |2 doi |
| 024 | 7 | _ | |a pmid:31919373 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC6952370 |2 pmc |
| 024 | 7 | _ | |a altmetric:73787594 |2 altmetric |
| 037 | _ | _ | |a DZNE-2020-00397 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 500 |
| 100 | 1 | _ | |a Marouf, Mohamed |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. |
| 260 | _ | _ | |a [London] |c 2020 |b Nature Publishing Group UK |
| 264 | _ | 1 | |3 online |2 Crossref |b Springer Science and Business Media LLC |c 2020-01-09 |
| 264 | _ | 1 | |3 print |2 Crossref |b Springer Science and Business Media LLC |c 2020-12-01 |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1713795813_6654 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data. cscGAN learns non-linear gene-gene dependencies from complex, multiple cell type samples and uses this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types. |
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| 542 | _ | _ | |i 2020-01-09 |2 Crossref |u https://creativecommons.org/licenses/by/4.0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 650 | _ | 7 | |a RNA |0 63231-63-0 |2 NLM Chemicals |
| 650 | _ | 2 | |a Algorithms |2 MeSH |
| 650 | _ | 2 | |a Animals |2 MeSH |
| 650 | _ | 2 | |a Biomedical Research: methods |2 MeSH |
| 650 | _ | 2 | |a Computer Simulation |2 MeSH |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Mice |2 MeSH |
| 650 | _ | 2 | |a Models, Theoretical |2 MeSH |
| 650 | _ | 2 | |a Neural Networks, Computer |2 MeSH |
| 650 | _ | 2 | |a RNA: genetics |2 MeSH |
| 650 | _ | 2 | |a RNA-Seq: methods |2 MeSH |
| 700 | 1 | _ | |a Machart, Pierre |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Bansal, Vikas |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Kilian, Christoph |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Magruder, Daniel S. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Krebs, Christian F. |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Bonn, Stefan |0 P:(DE-2719)2810547 |b 6 |e Last author |u dzne |
| 773 | 1 | 8 | |a 10.1038/s41467-019-14018-z |b : Springer Science and Business Media LLC, 2020-01-09 |n 1 |p 166 |3 journal-article |2 Crossref |t Nature Communications |v 11 |y 2020 |x 2041-1723 |
| 773 | _ | _ | |a 10.1038/s41467-019-14018-z |g Vol. 11, no. 1, p. 166 |0 PERI:(DE-600)2553671-0 |n 1 |p 166 |t Nature Communications |v 11 |y 2020 |x 2041-1723 |
| 856 | 4 | _ | |y OpenAccess |u https://pub.dzne.de/record/145037/files/DZNE-2020-00397.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://pub.dzne.de/record/145037/files/DZNE-2020-00397.pdf?subformat=pdfa |
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| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 6 |6 P:(DE-2719)2810547 |
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