An Integrated Tool for Imputing and Evaluating the Performance of Single-Cell RNA Sequencing Imputation

Single-cell RNA sequencing (scRNA-seq) technology provides insights into cellular heterogeneity at the single-cell level. However, inherent limitations in sequencing technology and the stochastic nature of gene expression can lead to inaccurate reporting of specific genes, resulting in so-called "dropout" events. These events occur when genes that should be expressed in the same cell type are mistakenly reported as absent. Such absence of gene expression can lead to errors in downstream analysis. Researchers have sought to recover dropout information using various techniques to address this issue. Nevertheless, determining the optimal method requires a benchmarking process. Although some benchmarking results have been reported, reproducing them can be challenging due to the multiple factors involved. This paper presents the scRNA-Imputation and Benchmarking Tool (scRNA-IBT), which facilitates the utilization and benchmarking of imputation methods. The tool offers a flexible extension of method and metric plugins, ensuring user-friendly operation and reproducibility.