Variable combinatorial gap-filling method for single-cell RNA-seq data

With the increasing development of single-cell RNA sequencing technology, a huge amount of sequencing data has emerged. The use of computational methods to fill in the gene expression information in scRNA-seq data is not only an important guide for gene regulatory network construction, embryonic development, and neurological research in the brain but also provides an important basis for drug development and clinical medicine. In this paper, we propose a variable combination of single-cell gap-filling algorithms with high gap-filling accuracy and fast computation speed through the comprehensive study and analysis of image repair technology and single-cell gap-filling algorithm. The experiments demonstrate that the U-net-based gap-filling method proposed in this paper has high accuracy in recovering gene expression values, can reduce the analysis errors caused by dropout events, and applies to large-scale data sets. In summary, the variable combinatorial gap-filling method for single-cell RNA-seq data proposed in this paper can effectively improve the results of downstream analysis and promote the development of research in the field of RNA sequencing data.