R package for manuscript "ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets"
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901890119237632 |
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| author | Zheng, Ye |
| author_facet | Zheng, Ye |
| contents | <p>Cellular Indexing of Transcriptomes and Epitopes by Sequencing (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, 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 an antibody titration study, a published COVID-19 CITE-seq dataset, and a human hematopoietic progenitors study allowed for identifying previously undetected phenotype-associated markers, illustrating a broad utility in biological applications.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15478055 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | R package for manuscript "ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets" Zheng, Ye CITE-seq Normalization non-parametric functional data analysis R <p>Cellular Indexing of Transcriptomes and Epitopes by Sequencing (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, 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 an antibody titration study, a published COVID-19 CITE-seq dataset, and a human hematopoietic progenitors study allowed for identifying previously undetected phenotype-associated markers, illustrating a broad utility in biological applications.</p> |
| title | R package for manuscript "ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets" |
| topic | CITE-seq Normalization non-parametric functional data analysis R |
| url | https://doi.org/10.5281/zenodo.15478055 |