ArticlePatterns (New York, N.Y.)2021
EmptyNN: A neural network based on positive and unlabeled learning to remove cell-free droplets and recover lost cells in scRNA-seq data.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- ProDTI: prior-guided positive-unlabeled learning for robust drug-target interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- QClus: a droplet filtering algorithm for enhanced snRNA-seq data quality in challenging samples.Nucleic acids research · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Rank Aggregation Methods and Tools in Genomic Data Analysis.Current genomics · 2025Review
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- EmptyDropsMultiome discriminates real cells from background in single-cell multiomics assays.Genome biology · 2024Article
- A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD).Entropy (Basel, Switzerland) · 2024Article
- Automatic quality control of single-cell and single-nucleus RNA-seq using valiDrops.NAR genomics and bioinformatics · 2023Article
- spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content.Nature communications · 2023Article
- Vaeda computationally annotates doublets in single-cell RNA sequencing data.Bioinformatics (Oxford, England) · 2023Article
- DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data.Genome biology · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Droplet-based single-cell RNA sequencing (scRNA-seq) has significantly increased the number of cells profiled per experiment and revolutionized the study of individual transcriptomes. However, to maximize the biological signal, robust computational methods are needed to distinguish cell-free from cell-containing droplets. Here, we introduce a novel cell-calling algorithm called EmptyNN, which trains a neural network based on positive-unlabeled learning for improved filtering of barcodes. For benchmarking purposes, we leveraged cell hashing and genetic variation to provide ground truth. EmptyNN accurately removed cell-free droplets while recovering lost cell clusters, and achieved an area under the receiver operating characteristics of 94.73% and 96.30%, respectively. Comparisons to current state-of-the-art cell-calling algorithms demonstrated the superior performance of EmptyNN. EmptyNN was further applied to a single-nucleus RNA sequencing (snRNA-seq) dataset and showed good performance. Therefore, EmptyNN represents a powerful tool to enhance both scRNA-seq and snRNA-seq quality control analyses.
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