Abstract
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for studying cellular heterogeneity and complexity. However, dropout events in single-cell RNA-seq data severely hinder the effectiveness and accuracy of downstream analysis. Therefore, data preprocessing with imputation methods is crucial to scRNA-seq analysis. RESULTS: To address the issue of oversmoothing in smoothing-based imputation methods, the presented AcImpute, an unsupervised method that enhances imputation accuracy by constraining the smoothing weights among cells for genes with different expression levels. Compared with nine other imputation methods in cluster analysis and trajectory inference, the experimental results can demonstrate that AcImpute effectively restores gene expression, preserves inter-cell variability, preventing oversmoothing and improving clustering and trajectory inference performance. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/Liutto/AcImpute.