ArticleGenome biology2019
DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
149 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
- An imputation method for single-cell RNA sequencing data based on a parallel gated recurrent unit neural network.Quantitative biology (Beijing, China) · 2026Article
- A Method for Imputing scRNA-seq Data Based on Mobile Graph Convolutional Generative Adversarial Networks.Biochemical genetics · 2026Article
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- MMP3C v2: a network-based framework decoding metabolic plasticity in rheumatoid arthritis, enabling accurate diagnosis and uncovering cell-type-specific metabolic rewiring.Briefings in bioinformatics · 2026Article
- Spatial Mapping of Single Cells via Correlation and Importance Between Cells and Spots.Interdisciplinary sciences, computational life sciences · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses.iScience · 2026Article
- Prior-guided factorization for reliable imputation of scRNA-seq data.PLoS computational biology · 2026Article
- scAURA: Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture for Self-Supervised Clustering of Single-Cell Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Imputing missing values in single-cell RNA-sequencing data: a statistical and machine learning-based approach.Briefings in bioinformatics · 2026Article
- GSCI: A Generative and Sparse Compressed Sensing Imputation Framework for Single-Cell RNA-Sequencing Dropout Recovery.Computational and structural biotechnology journal · 2026Article
- DeepIMB: Imputation of non-biological zero counts in microbiome data.Genes & genomics · 2026Article
- DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data.Computational and structural biotechnology journal · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Denoising single-cell RNA-seq data with a deep learning-embedded statistical framework.BMC bioinformatics · 2025Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
89 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson's correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at https://github.com/lanagarmire/DeepImpute .
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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.