ArticleBMC bioinformatics2018
DrImpute: imputing dropout events in single cell RNA sequencing data.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 1 of them a synthesis that pooled it.
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Who cites it
160 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Immune context governs stage-specific responses to depleting anti-PD-1 antibody in type 1 diabetes.Cell biomaterials · 2026Article
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- Prior-guided factorization for reliable imputation of scRNA-seq data.PLoS computational biology · 2026Article
- scIRT: Imputation and Dimensionality Reduction for Single-Cell RNA-Seq Data by Combining NMF with SMOTE.International journal of molecular sciences · 2026Article
- Imputing missing values in single-cell RNA-sequencing data: a statistical and machine learning-based approach.Briefings in bioinformatics · 2026Article
- A clustering method for single-cell RNA sequencing data based on denoising and masking learning.Frontiers in bioinformatics · 2026Article
- D3Impute: Dropout-aware discrimination, distribution-aware modeling, and density-guide imputation for scRNA-seq data.PLoS computational biology · 2025Article
- Single-cell foundation models: bringing artificial intelligence into cell biology.Experimental & molecular medicine · 2025Review
- Advancing biological understanding of cellular senescence with computational multiomics.Nature genetics · 2025Review
- BiAEImpute: a robust bidirectional autoencoder framework for High-fidelity dropout imputation in single-cell transcriptomics.BMC genomics · 2025Article
- scVGAMF: a novel imputation method for scRNA-seq data by integrating linear and non-linear features.Briefings in bioinformatics · 2025Article
- Advances in Single-Cell Sequencing for Infectious Diseases: Progress and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- DropDAE: Denosing Autoencoder with Contrastive Learning for Addressing Dropout Events in scRNA-seq Data.Bioengineering (Basel, Switzerland) · 2025Article
- Discordant effects of maternal age on the human MII oocyte transcriptome.Molecular human reproduction · 2025Article
- Super-enhancer-mediated circRNAs exhibit high splicing circularization diversity and transcriptional activity.Nucleic acids research · 2025Article
- Imputing single-cell protein abundance in multiplex tissue imaging.Nature communications · 2025Article
- Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation.BMC bioinformatics · 2025Article
- A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics.Nature communications · 2025Article
100 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events.
resultsWe develop DrImpute to impute dropout events in scRNA-seq data. We show that DrImpute has significantly better performance on the separation of the dropout zeros from true zeros than existing imputation algorithms. We also demonstrate that DrImpute can significantly improve the performance of existing tools for clustering, visualization and lineage reconstruction of nine published scRNA-seq datasets.
conclusionsDrImpute can serve as a very useful addition to the currently existing statistical tools for single cell RNA-seq analysis. DrImpute is implemented in R and is available at https://github.com/gongx030/DrImpute .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.