Single-cell RNA-seq Imputation using Generative Adversarial Networks [article]

Yungang Xu, Zhigang Zhang, Lei You, Jiajia Liu, Zhiwei Fan, Xiaobo zhou
2020 bioRxiv   pre-print
Single-cell RNA-seq (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, often termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods
more » ... ffer oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks for scRNA-seq imputation (scIGANs), which uses generated realistic rather than observed cells to avoid these limitations and the powerless for rare cells. Evaluations based on a variety of simulated and real scRNA-seq datasets demonstrate that scIGANs is effective for dropout imputation and enhancing various downstream analysis. ScIGANs is also scalable and robust to small datasets that have few genes with low expression and/or cell-to-cell variance.
doi:10.1101/2020.01.20.913384 fatcat:vzebhtgo2bcptojeivaqan5fwq