ArticleBMC bioinformatics2023
Evaluating imputation methods for single-cell RNA-seq data.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Single cell transcriptomic analysis by deep learning identifies novel microglial transcription regulations in Alzheimer's Disease.AI in neuroscience · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses.iScience · 2026Article
- miRNA regulation in brain tissue space: the 3'UTR perspective.RNA (New York, N.Y.) · 2026Review
- Article
- Evaluating imputation methods for accurate estimation of cell population fractions in single-cell RNA sequencing.NAR genomics and bioinformatics · 2026Article
- A hitchhiker's guide to single-cell epigenomics: Methods and applications for cancer research.International journal of cancer · 2026Review
- A computational framework to dissect imputation strategies for single-cell histone modification data.NAR genomics and bioinformatics · 2025Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Systematic correlation analysis of human CD molecules reveals upregulated co-expression of CD58, CD63, and CD147 in patients with primary Sjögren's syndrome.Immunologic research · 2025Article
- Uncovering Cellular Interactome Drivers of Immune Checkpoint Inhibitor Response in Advanced Melanoma Patients.Cellular and molecular bioengineering · 2025Article
- scTsI: an effective two-stage imputation method for single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- A graph neural network that combines scRNA-seq and protein-protein interaction data.Nature methods · 2025Article
- scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.Nature methods · 2025Article
- An in-depth benchmark framework for evaluating single cell RNA-seq dropout imputation methods and the development of an improved algorithm afMF.Clinical and translational medicine · 2025Article
- New Multiomic Studies Shed Light on Cellular Diversity and Neuronal Susceptibility in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Conserved transcription factors coordinate synaptic gene expression through repression.bioRxiv : the preprint server for biology · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- PhyImpute and UniFracImpute: two imputation approaches incorporating phylogeny information for microbial count data.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSingle-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms.
resultsIn this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain.
conclusionsIn summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.
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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 Socratic graph.