Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks.

ABSTRACT: 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

SUBMITTER: Marouf M 

PROVIDER: S-EPMC6952370 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature