  Single-cell RNA sequencing (scRNA-seq) determines RNA expression at
single-cell resolution. It provides a powerful tool for studying immunity,
regulation, and other life activities of cells. However, due to the limitations
of the sequencing technique, the scRNA-seq data are represented with sparsity,
whichcontains missing gene values, i.e., zero values, called dropout.
Therefore, it is necessary to impute missing values before analyzing scRNA-seq
data. However, existing imputation computation methods often only focus on the
identification of technical zeros or imputing all zeros based on cell
similarity. This study proposes a new method (SFAG) to reconstruct the gene
expression relationship matrix by usinggraph regularization technology to
preserve the high-dimensional manifold information of the data, andto mine the
relationship between genes and cells in the data, and then uses a method of
averaging the clustering results to fill in the identified technical zeros.
Experimental results show that SFAGcan helpimprove downstream analysis and
reconstruct cell trajectory
