ArticleNature communications2022
Normalizing and denoising protein expression data from droplet-based single cell profiling.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers.
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Who cites it
130 citing papers in PubMed.
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- Cross-Modal Denoising and Integration of Spatial Multi-Omics Data with CANDIES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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- SARS-CoV-2 infection and vaccination elicit distinct pharyngeal mucosal B cell responses in children.Nature communications · 2026Article
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- Modeling human B cell development with pluripotent stem cells.bioRxiv : the preprint server for biology · 2026Article
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- Single-cell analysis highlights the significance of malignant cell IFN/MHC-II for immunotherapy response in head and neck squamous cell carcinoma.Cell reports. Medicine · 2026Article
- Multi-omic profiling of human antibody-secreting cells reveals diverse subsets sustain durable humoral immunity.bioRxiv : the preprint server for biology · 2026Article
- Article
70 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called "dsb" (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at "dsb [ https://cran.r-project.org/package=dsb ]".
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