TY - JOUR T1 - Realistic <em>in silico</em> generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks JF - bioRxiv DO - 10.1101/390153 SP - 390153 AU - Mohamed Marouf AU - Pierre Machart AU - Vikas Bansal AU - Christoph Kilian AU - Daniel S. Magruder AU - Christian F. Krebs AU - Stefan Bonn Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/08/13/390153.abstract N2 - 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 (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use 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. cscGANs outperform 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.scGANsingle-cell Generative Adversarial NetworkcscGANconditional single-cell Generative Adversarial NetworkRNA-seqRiboNucleic Acid sequencingscRNA-seqsingle-cell RiboNucleic Acid sequencingGANGenerative Adversarial NetworkPBMCPeripheral Blood MonoCytest-SNEt-distributed Stochastic Neighbor EmbeddingRFRandom ForestROCReceiver Operating CharacteristicAUCArea Under the (ROC) CurveGPUGraphics Processing UnitLSNLibrary Size NormalizationUMIUnique Molecular IdentifiersPCAPrincipal Component AnalysisPCsPrincipal ComponentsCNNConvolutional Neural NetworkFCFully-ConnectedReLURectified Linear UnitACGANAuxiliary Classifier Generative Adversarial Network ER -