biostudies-literatureUnknown18(sup1)Wu HThe high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distributRNA biology172-181https://www.ebi.ac.uk/biostudies/studies/S-EPMC8682979biostudies-literatureFRMC: a fast and robust method for the imputation of scRNA-seq data.PMC8682979Xiang RWang XChu MZhou KWu HfalseFRMC: a fast and robust method for the imputation of scRNA-seq data.The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distribut2021-01-01T00:00:00Z2021 Oct2026-05-09T19:35:11.226Z2025-04-21T14:52:20.637ZS-EPMC86829793445971910.1080/15476286.2021.1960688