论文标题
SCGNN：SCRNA-SEQ辍学通过诱导的分层单元格相图
scGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph
论文作者
论文摘要
单细胞RNA测序提供了了解生物系统的巨大见解。但是，辍学的噪音会破坏下游的生物学分析。因此，希望准确地将辍学物归为。在这项工作中，我们通过在诱导的分层单元格相似图上应用瓶颈图卷积神经网络，提出了一种简单而强大的辍学方法（SCGNN）。我们显示，SCGNN在三个数据集中对最先进的基线具有竞争性能，并且可以改善下游分析。
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.
