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 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.
Highlights
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
Data modeling relies on a thorough understanding of the priors on invariants underlying the production of such data, current methods of choice for photorealistic image generation rely on deep learning-based generative adversarial networks (GANs)[5,6,7,8] and variational autoencoders (VAEs)[9,10]
As a proof of concept that realistic in silico generation could potentially be applied to biomedical omics data, we focus on the generation of single-cell RNA sequencing data using GANs. scRNA sequencing has made it possible to evaluate genome-wide gene expression of thousands to millions of cells in a single experiment[11]
Summary
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. As a proof of concept that realistic in silico generation could potentially be applied to biomedical omics data, we focus on the generation of single-cell RNA (scRNA) sequencing data using GANs. scRNA sequencing has made it possible to evaluate genome-wide gene expression of thousands to millions of cells in a single experiment[11]. This detailed information across genes and cells opens the door to a much deeper understanding of cell type heterogeneity in a tissue, cell differentiation, and cell type-specific disease etiology
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have