  Single-cell RNA sequencing provides tremendous insights to understand
biological systems. However, the noise from dropout can corrupt the downstream
biological analysis. Hence, it is desirable to impute the dropouts accurately.
In this work, we propose a simple and powerful dropout imputation method
(scGNN) by applying a bottlenecked Graph Convolutional Neural Network on an
induced hierarchical cell similarity graph. We show scGNN has competitive
performance against state-of-the-art baselines across three datasets and can
improve downstream analysis.
