ArticleBioinformatics (Oxford, England)2023
TsImpute: an accurate two-step imputation method for single-cell RNA-seq data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- SpaBiT: enhancing spatial transcriptomics resolution via bidirectional attention transformers.Bioinformatics (Oxford, England) · 2026Article
- Hypergraph representations of single-cell RNA sequencing data for improved cell clustering.Bioinformatics (Oxford, England) · 2026Article
- D3Impute: Dropout-aware discrimination, distribution-aware modeling, and density-guide imputation for scRNA-seq data.PLoS computational biology · 2025Article
- Self-Supervised Graph Representation Learning for Single-Cell Classification.Interdisciplinary sciences, computational life sciences · 2025Article
- scVGAMF: a novel imputation method for scRNA-seq data by integrating linear and non-linear features.Briefings in bioinformatics · 2025Article
- Imputing single-cell protein abundance in multiplex tissue imaging.Nature communications · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- PbImpute: Precise Zero Discrimination and Balanced Imputation in Single-Cell RNA Sequencing Data.Journal of chemical information and modeling · 2025Article
- CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data.Briefings in bioinformatics · 2024Article
- scTCA: a hybrid Transformer-CNN architecture for imputation and denoising of scDNA-seq data.Briefings in bioinformatics · 2024Article
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3 authors.
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Abstract
motivationSingle-cell RNA sequencing (scRNA-seq) technology has enabled discovering gene expression patterns at single cell resolution. However, due to technical limitations, there are usually excessive zeros, called "dropouts," in scRNA-seq data, which may mislead the downstream analysis. Therefore, it is crucial to impute these dropouts to recover the biological information.
resultsWe propose a two-step imputation method called tsImpute to impute scRNA-seq data. At the first step, tsImpute adopts zero-inflated negative binomial distribution to discriminate dropouts from true zeros and performs initial imputation by calculating the expected expression level. At the second step, it conducts clustering with this modified expression matrix, based on which the final distance weighted imputation is performed. Numerical results based on both simulated and real data show that tsImpute achieves favorable performance in terms of gene expression recovery, cell clustering, and differential expression analysis. AVAILABILITY AND IMPLEMENTATION: The R package of tsImpute is available at https://github.com/ZhengWeihuaYNU/tsImpute.
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