ArticleNature communications2018
An accurate and robust imputation method scImpute for single-cell RNA-seq data.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 374 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
374 citing papers in PubMed, 2 syntheses or guidelines 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
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- An imputation method for single-cell RNA sequencing data based on a parallel gated recurrent unit neural network.Quantitative biology (Beijing, China) · 2026Article
- scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.Interdisciplinary sciences, computational life sciences · 2026Article
- Decoding Skeletal Biology Through Transcriptomics: Insights from Bulk, Single-Cell, Spatial, and Multi-Omics Approaches.International journal of molecular sciences · 2026Review
- SpaDiff denoises sequence-based spatial transcriptomics via diffusion process.Cell reports methods · 2026Article
- E12.5 pancreatic bud-derived cells are detected in recipient pancreata after intraplacental transplantation, but Pdx1-deficient recipients lack a macroscopically detectable pancreas.Scientific reports · 2026Article
- Single cell transcriptomic analysis by deep learning identifies novel microglial transcription regulations in Alzheimer's Disease.AI in neuroscience · 2026Article
- A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation.Briefings in bioinformatics · 2026Article
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- Self-supervised graph contrastive learning for scRNA-seq clustering.Journal of translational medicine · 2026Article
- scTACL: a multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.Interdisciplinary sciences, computational life sciences · 2026Article
- SCOTCH: isoform-level characterization of gene expression through long-read single-cell RNA sequencing.Nature communications · 2026Article
- Single-Cell Analysis of Preeclamptic Cord Blood Mononuclear Cells Revealed Activation of Heme-Associated Signalling Pathways.Journal of cellular and molecular medicine · 2026Article
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A unique malignant cell type per patient tumor encoded in each cancer cell transcriptome.iScience · 2026Article
- Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck.BMC biology · 2026Article
- Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses.iScience · 2026Article
314 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.
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Registered trials
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.