Evaluating imputation methods for accurate estimation of cell population fractions in single-cell RNA sequencing.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic analysis, enabling high-resolution profiling of gene expression across individual cells. However, technical limitations, particularly dropout events, still hinder the accurate quantification of gene expression heterogeneity, resulting in an underestimation of gene-expressing cell proportions. Numerous computational methods have been developed to impute missing expression signals. In this study, we systematically evaluated eight prominent imputation algorithms-MAGIC, SAVER, scVI, DCA, scBiG, kNN-smoothing, scImpute, and ALRA-using two simulated datasets and a deeply sequenced reference dataset. We defined conditions that make it possible to improve estimates of cell population sizes and demonstrate the utility of imput

SUBMITTER: Valyaeva AA 

PROVIDER: S-EPMC12770975 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature