An efficient scRNA-seq dropout imputation method using graph attention network

• Published in: BMC bioinformatics 2021-12, Vol.22 (1), p.582-582, Article 582
• Main Authors: Xu, Chenyang, Cai, Lei, Gao, Jingyang
• Format: Article
• Language: English
• Subjects: Aggregates; Analysis; Clustering; Convolution; Correlation coefficient; Correlation coefficients; Datasets; Deep learning; Dropout imputation; Euclidean geometry; Gene expression; Graph attention convolution; Heterogeneity; Information processing; Methodology; Methods; Neural networks; Noise; Noise control; Noise reduction; RNA sequencing; scRNA-seq; Spatial data
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04493-x

Single-cell sequencing technology can address the amount of single-cell library data at the same time and display the heterogeneity of different cells. However, analyzing single-cell data is a computationally challenging problem. Because there are low counts in the gene expression region, it has a high chance of recognizing the non-zero entity as zero, which are called dropout events. At present, the mainstream dropout imputation methods cannot effectively recover the true expression of cells from dropout noise such as DCA, MAGIC, scVI, scImpute and SAVER. In this paper, we propose an autoencoder structure network, named GNNImpute. GNNImpute uses graph attention convolution to aggregate multi-level similar cell information and implements convolution operations on non-Euclidean space on scRNA-seq data. Distinct from current imputation tools, GNNImpute can accurately and effectively impute the dropout and reduce dropout noise. We use mean square error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC) and Cosine similarity (CS) to measure the performance of different methods with GNNImpute. We analyze four real datasets, and our results show that the GNNImpute achieves 3.0130 MSE, 0.6781 MAE, 0.9073 PCC and 0.9134 CS. Furthermore, we use Adjusted rand index (ARI) and Normalized mutual information (NMI) to measure the clustering effect. The GNNImpute achieves 0.8199 (ARI) and 0.8368 (NMI), respectively. In this investigation, we propose a single-cell dropout imputation method (GNNImpute), which effectively utilizes shared information for imputing the dropout of scRNA-seq data. We test it with different real datasets and evaluate its effectiveness in MSE, MAE, PCC and CS. The results show that graph attention convolution and autoencoder structure have great potential in single-cell dropout imputation.