ArticleScientific reports2018
AutoImpute: Autoencoder based imputation of single-cell RNA-seq data.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
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Who cites it
78 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
- Prior-guided factorization for reliable imputation of scRNA-seq data.PLoS computational biology · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- scGACL: a generative adversarial network with multi-scale contrastive learning for accurate single-cell RNA sequencing imputation.Briefings in bioinformatics · 2026Article
- DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data.Computational and structural biotechnology journal · 2026Article
- New algorithms for unsupervised cell clustering from scRNA-seq data.Bioinformatics advances · 2026Article
- GPU-accelerated, self-optimizing processing for 3D multiplexed iterative RNA-FISH experiments.bioRxiv : the preprint server for biology · 2025Article
- GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge.Briefings in bioinformatics · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Ocelli: an open-source tool for the analysis and visualization of developmental multimodal single-cell data.NAR genomics and bioinformatics · 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
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Modal-nexus auto-encoder for multi-modality cellular data integration and imputation.Nature communications · 2024Article
- SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders.BMC bioinformatics · 2024Article
- scRNMF: An imputation method for single-cell RNA-seq data by robust and non-negative matrix factorization.PLoS computational biology · 2024Article
- BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell imputation.Briefings in bioinformatics · 2024Article
- scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.Briefings in bioinformatics · 2024Article
- scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder.Briefings in bioinformatics · 2024Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of single-cell RNA sequencing (scRNA-seq) technologies has enabled us to measure the expression levels of thousands of genes at single-cell resolution. However, insufficient quantities of starting RNA in the individual cells cause significant dropout events, introducing a large number of zero counts in the expression matrix. To circumvent this, we developed an autoencoder-based sparse gene expression matrix imputation method. AutoImpute, which learns the inherent distribution of the input scRNA-seq data and imputes the missing values accordingly with minimal modification to the biologically silent genes. When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability.
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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.