An imputation method for single-cell RNA sequencing data based on a parallel gated recurrent unit neural network.

Single-cell RNA sequencing (scRNA-seq) generates high-dimensional transcriptomic data but is severely affected by dropout events, which obscure true gene-gene relationships. These missing values introduce bias into downstream analyses, complicate data processing, and reduce analytical efficiency. Therefore, accurate imputation of dropout events is essential for recovering genuine expression signals and improving the reliability of scRNA-seq studies. In this work, we propose a novel scRNA-seq data imputation method, imputation with a parallel gated recurrent unit neural network (IPGRU), based on a parallel gated recurrent unit (GRU) neural network. IPGRU reformulates the original multifeature prediction problem into multiple single-feature prediction tasks, thereby substantially reducing the complexity of the imputation process. By exploiting dependencies between expressed and missing genes, the GRU hidden states are employed to model gene expression patterns and predict dropout events, enabling effective correction of missing values and improving data completeness. Experimental results demonstrate that IPGRU achieves high accuracy in imputing dropout events and significantly enhances the integrity of scRNA-seq data. Moreover, the imputed data provide more reliable inputs for downstream analyses, including cell clustering and inference of differentiation trajectories. IPGRU offers a robust and efficient solution for scRNA-seq data imputation, improving the accuracy and reliability of downstream biological analyses. This method has strong potential to facilitate biological discovery and advance precision medicine.