ArticleBMC genomics2023
FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Systematic benchmarking of ambient RNA decontamination tools to advance precision in single-cell transcriptomic analysis.Nature communications · 2026Article
- Cellular hallmarks and aging clock of the human lung parenchyma.Nature communications · 2026Article
- TGFβ signaling mediates microglial resilience to spatiotemporally restricted myelin degeneration.Nature neuroscience · 2026Article
- 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
- Single-cell transcriptional profiling in Arabidopsis root exposed to boron toxicity at seedling stages.Plant molecular biology · 2025Article
- Accelerated Tempo of Cortical Neurogenesis in Down Syndrome.bioRxiv : the preprint server for biology · 2025Article
- Non-variable RNA deletion using the CRISPR-Cas9 technique demonstrated improved outcomes in human intestine single-cell RNA sequencing data, even at half sequencing depths.Genomics & informatics · 2025Article
- Deciphering the cell type-specific and zonal distribution of drug-metabolizing enzymes, transporters, and transcription factors in livers of mice using single-cell transcriptomics.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- Understanding and mitigating the impact of ambient mRNA contamination in single-cell RNA-sequencing analysis.PloS one · 2025Article
- Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations.Clinical and experimental immunology · 2024Review
- A global transcriptional atlas of the effect of acute sleep deprivation in the mouse frontal cortex.iScience · 2024Article
- Airway epithelial cell response to RSV is mostly impaired in goblet and multiciliated cells in asthma.Thorax · 2024Article
- Spatiotemporal molecular dynamics of the developing human thalamus.Science (New York, N.Y.) · 2023Article
- Article
- Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications.Military Medical Research · 2022Review
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Authors and funding
11 authors.
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Abstract
Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.
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