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000145037 041__ $$aEnglish
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000145037 1001_ $$0P:(DE-HGF)0$$aMarouf, Mohamed$$b0
000145037 245__ $$aRealistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks.
000145037 260__ $$a[London]$$bNature Publishing Group UK$$c2020
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000145037 520__ $$aA fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data. cscGAN learns non-linear gene-gene dependencies from complex, multiple cell type samples and uses this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.
000145037 536__ $$0G:(DE-HGF)POF3-342$$a342 - Disease Mechanisms and Model Systems (POF3-342)$$cPOF3-342$$fPOF III$$x0
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000145037 650_7 $$063231-63-0$$2NLM Chemicals$$aRNA
000145037 650_2 $$2MeSH$$aAlgorithms
000145037 650_2 $$2MeSH$$aAnimals
000145037 650_2 $$2MeSH$$aBiomedical Research: methods
000145037 650_2 $$2MeSH$$aComputer Simulation
000145037 650_2 $$2MeSH$$aHumans
000145037 650_2 $$2MeSH$$aMice
000145037 650_2 $$2MeSH$$aModels, Theoretical
000145037 650_2 $$2MeSH$$aNeural Networks, Computer
000145037 650_2 $$2MeSH$$aRNA: genetics
000145037 650_2 $$2MeSH$$aRNA-Seq: methods
000145037 7001_ $$0P:(DE-HGF)0$$aMachart, Pierre$$b1
000145037 7001_ $$0P:(DE-HGF)0$$aBansal, Vikas$$b2
000145037 7001_ $$0P:(DE-HGF)0$$aKilian, Christoph$$b3
000145037 7001_ $$0P:(DE-HGF)0$$aMagruder, Daniel S.$$b4
000145037 7001_ $$0P:(DE-HGF)0$$aKrebs, Christian F.$$b5
000145037 7001_ $$0P:(DE-2719)2810547$$aBonn, Stefan$$b6$$eLast author$$udzne
000145037 77318 $$2Crossref$$3journal-article$$a10.1038/s41467-019-14018-z$$b : Springer Science and Business Media LLC, 2020-01-09$$n1$$p166$$tNature Communications$$v11$$x2041-1723$$y2020
000145037 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-019-14018-z$$gVol. 11, no. 1, p. 166$$n1$$p166$$tNature Communications$$v11$$x2041-1723$$y2020
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