ArticleInternational journal of molecular sciences2023
Epi-Impute: Single-Cell RNA-seq Imputation via Integration with Single-Cell ATAC-seq.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.Bioinformatics (Oxford, England) · 2026Article
- Integrating multi-omics and experimental techniques to decode ubiquitinated protein modifications in hepatocellular carcinoma.Frontiers in pharmacology · 2025Article
- AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification.Bioinformatics (Oxford, England) · 2024Article
- scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting.Briefings in bioinformatics · 2023Article
- Medical Genetics, Genomics and Bioinformatics-2022.International journal of molecular sciences · 2023Article
- Research Topics of the Bioinformatics of Gene Regulation.International journal of molecular sciences · 2023Article
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Authors and funding
5 authors.
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Abstract
Single-cell RNA-seq data contains a lot of dropouts hampering downstream analyses due to the low number and inefficient capture of mRNAs in individual cells. Here, we present Epi-Impute, a computational method for dropout imputation by reconciling expression and epigenomic data. Epi-Impute leverages single-cell ATAC-seq data as an additional source of information about gene activity to reduce the number of dropouts. We demonstrate that Epi-Impute outperforms existing methods, especially for very sparse single-cell RNA-seq data sets, significantly reducing imputation error. At the same time, Epi-Impute accurately captures the primary distribution of gene expression across cells while preserving the gene-gene and cell-cell relationship in the data. Moreover, Epi-Impute allows for the discovery of functionally relevant cell clusters as a result of the increased resolution of scRNA-seq data due to imputation.
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