ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets
Open accessAbstract
CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm ( https://github.com/yezhengSTAT/ADTnorm ), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.
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Most ADT markers show a bimodal distribution with a negative peak for nonspecific antibody binding and a positive peak resembling enrichment of cell-surface proteins in specific cell types.
“Most markers exhibit a bimodal distribution with a ‘negative’ (low count) peak for nonspecific antibody binding and a ‘positive’ peak that resembles enrichment of cell-surface proteins in specific cell types160.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [160]
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Self-citationADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets
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Best practices for single-cell analysis across modalities
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- Authors
- Zheng, Y., Caron, D. P., Kim, J. Y., Jun, S.-H., Tian, Y., Florian, M., Stuart, K. D., Sims, P. A., & Gottardo, R.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Columbia University (US)
- Fred Hutch Cancer Center (US)
- University of Rochester Medicine (US)
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2022-05-01
- Volume
- 16
- Issue
- 1
- Pages
- 5852-5852
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11261982
- OpenAlex
- W4411882241
- Cited by
- 18
- References
- 59
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 3 · 2023: 2 · 2022: 1
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by-nc-nd)
- IRIS (submittedVersion, cc-by-nc-nd)
- PubMed Central (submittedVersion, cc-by)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- U.S. Department of Health and Human Services (HG012797)
- Fred Hutchinson Cancer Research Center
- Université de Lausanne
- National Institutes of Health (1s10od028685-01, HG012797)
- National Human Genome Research Institute (K99HG012797, R00HG012797)
- National Institute of Allergy and Infectious Diseases (T32AI106711, U19AI128914, U19AI128949)
- Office of Research Infrastructure Programs, National Institutes of Health (S10OD028685)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- CITE-seq, surface protein expression, normalization, data integration, batch effects, single-cell proteomics, cell type annotation, antibody titration, COVID-19 pandemic, hematopoietic progenitor cells, multi-omics integration
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