ArticleGenome biology2023
The effect of background noise and its removal on the analysis of single-cell expression data.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Article
- Systematic benchmarking of ambient RNA decontamination tools to advance precision in single-cell transcriptomic analysis.Nature communications · 2026Article
- Palmitate promotes liver metastases by decreasing neutrophil antitumour behaviour.Nature metabolism · 2026Article
- CellBouncer, a unified toolkit for single-cell demultiplexing and ambient RNA analysis, reveals hominid mitochondrial incompatibilities.Cell genomics · 2026Article
- PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets.bioRxiv : the preprint server for biology · 2026Article
- Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments.Molecular biology reports · 2026Review
- High-quality acinar cell isolation enables single-cell analysis of healthy and injured pancreas.Cell reports methods · 2026Article
- Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics.iScience · 2026Article
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- SpatialCOC: an integrative framework for spatial continuous mapping and cross-omics correction in spatial multi-omics data.Nature communications · 2026Article
- An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease.Genome biology · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Integrated experimental and computational workflows for single-cell transcriptomics in plants.Plant methods · 2026Article
- Spatial transcriptomics reveal heterogeneous cell‒cell interactions among brain regions in cuprizone model consistent with multiple sclerosis lesions.Frontiers in bioinformatics · 2026Article
- Parallel single-cell host immune profiling and pathogen genomic characterization inFrontiers in cellular and infection microbiology · 2026Observational
- Artificial Intelligence (AI) and Liquid Biopsy Transforming Early Detection of Liver Metastases in Gastrointestinal Cancers.Current cancer drug targets · 2026Review
- Benchmarking computational decontamination of ambient RNA.Frontiers in bioinformatics · 2026Article
- Why "Where" Matters as Much as "How Much": Single-Cell and Spatial Transcriptomics in Plants.International journal of molecular sciences · 2025Review
- Article
- De Novo Gene Emergence: Summary, Classification, and Challenges of Current Methods.Genome biology and evolution · 2025Review
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Authors and funding
11 authors.
Funding
Abstract
backgroundIn droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events.
resultsHere, we characterize this background noise exemplified by three scRNA-seq and two snRNA-seq replicates of mouse kidneys. For each experiment, cells from two mouse subspecies are pooled, allowing to identify cross-genotype contaminating molecules and thus profile background noise. Background noise is highly variable across replicates and cells, making up on average 3-35% of the total counts (UMIs) per cell and we find that noise levels are directly proportional to the specificity and detectability of marker genes. In search of the source of background noise, we find multiple lines of evidence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure.
conclusionsOur findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.
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