scRFR: imputation of single-cell RNA-seq data based on recurrent feature inference

The area of single-cell RNA sequencing, or scRNA-seq, is expanding quickly and aids in the analysis of expression status. It provides a powerful tool for determining precise expression of tens of thousands of single cells, deciphering cell heterogeneity and cell subsets, etc. Nevertheless, the scRNA-seq data contains a great deal of biological and technical noise, and the associated analysis still has a long way to go. We introduced scRFR, a technique for imputation of scRNA-seq data based on recurrent feature reasoning for Image Inpainting, in order to address "dropout" noise seen in scRNA-seq data. It was discovered through testing that this algorithm has a greater imputation accuracy.