TY  - JOUR
AU  - Marouf, Mohamed
AU  - Machart, Pierre
AU  - Bansal, Vikas
AU  - Kilian, Christoph
AU  - Magruder, Daniel S.
AU  - Krebs, Christian F.
AU  - Bonn, Stefan
TI  - Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks.
JO  - Nature Communications
VL  - 11
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Nature Publishing Group UK
M1  - DZNE-2020-00397
SP  - 166
PY  - 2020
AB  - 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.
KW  - Algorithms
KW  - Animals
KW  - Biomedical Research: methods
KW  - Computer Simulation
KW  - Humans
KW  - Mice
KW  - Models, Theoretical
KW  - Neural Networks, Computer
KW  - RNA: genetics
KW  - RNA-Seq: methods
KW  - RNA (NLM Chemicals)
LB  - PUB:(DE-HGF)16
C6  - pmid:31919373
C2  - pmc:PMC6952370
DO  - DOI:10.1038/s41467-019-14018-z
UR  - https://pub.dzne.de/record/145037
ER  -