2024/02/15 by Linfeng Jiang, Yuan Zhu, Jiang, Linfeng +1
Biochemistry, Genetics and Molecular Biology · #Advanced biosensing and bioanalysis techniques #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Information Theory (cs.IT) #Molecular Biology Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2402.09755
openalex publication_date 2024/02/15 · openalex created_date 2024/02/18 · openalex updated_date 2026/07/28
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