Package: dsb 1.0.4
dsb: Normalize & Denoise Droplet Single Cell Protein Data (CITE-Seq)
This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mulè M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 <https://www.nature.com/articles/s41467-022-29356-8> for more details on the method.
Authors:
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dsb.pdf |dsb.html✨
dsb/json (API)
NEWS
# Install 'dsb' in R: |
install.packages('dsb', repos = c('https://niaid.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/niaid/dsb/issues
- cells_citeseq_mtx - Small example CITE-seq protein dataset for 87 surface protein in 2872 cells
- empty_drop_citeseq_mtx - Small example CITE-seq protein dataset for 87 surface protein in 8005 empty droplets
Last updated 5 months agofrom:1c53b4e541. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 13 2024 |
R-4.5-win | OK | Nov 13 2024 |
R-4.5-linux | OK | Nov 13 2024 |
R-4.4-win | OK | Nov 13 2024 |
R-4.4-mac | OK | Nov 13 2024 |
R-4.3-win | OK | Nov 13 2024 |
R-4.3-mac | OK | Nov 13 2024 |
Additional Topics - qualtile.clipping - scale.factor - Python and Bioc - multiplexing - multi batch - FAQ
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on Nov 13 2024.Last update: 2023-03-10
Started: 2022-03-02
End-to-end CITE-seq analysis workflow using dsb for ADT normalization and Seurat for multimodal clustering
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on Nov 13 2024.Last update: 2024-06-15
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Normalizing ADTs for datasets without empty droplets with the dsb function ModelNegativeADTnorm
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usingknitr::rmarkdown
on Nov 13 2024.Last update: 2022-03-14
Started: 2022-03-11
Understanding how the dsb method works
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on Nov 13 2024.Last update: 2022-03-14
Started: 2022-03-02