ArticleQuantitative biology (Beijing, China)2020
Imputation of single-cell gene expression with an autoencoder neural network.
Article in Quantitative biology (Beijing, China), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- An imputation method for single-cell RNA sequencing data based on a parallel gated recurrent unit neural network.Quantitative biology (Beijing, China) · 2026Article
- SCOTCH: isoform-level characterization of gene expression through long-read single-cell RNA sequencing.Nature communications · 2026Article
- A genetic algorithm-based framework for online sparse feature selection in data streams.Frontiers in big data · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.APL bioengineering · 2025Article
- [Imputation method for dropout in single-cell transcriptome data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- mlf-core: a framework for deterministic machine learning.Bioinformatics (Oxford, England) · 2023Article
- Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing.Biophysics reviews · 2023Review
- Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.Briefings in bioinformatics · 2023Article
- Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data.Computational and structural biotechnology journal · 2023Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- Statistics or biology: the zero-inflation controversy about scRNA-seq data.Genome biology · 2022Review
- Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.Briefings in bioinformatics · 2022Article
- NISC: Neural Network-Imputation for Single-Cell RNA Sequencing and Cell Type Clustering.Frontiers in genetics · 2022Article
- An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation.Current genomics · 2021Review
- Analysis of single-cell RNA sequencing data based on autoencoders.BMC bioinformatics · 2021Article
- Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining.NPJ systems biology and applications · 2021Article
- Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges.Frontiers in genetics · 2021Review
- Intelligent Health Care: Applications of Deep Learning in Computational Medicine.Frontiers in genetics · 2021Review
- A primer on machine learning techniques for genomic applications.Computational and structural biotechnology journal · 2021Review
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7 authors.
Funding
Abstract
backgroundSingle-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses.
methodsTo overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.
resultsOn both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.
conclusionsWe demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.
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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.