Normalizing and denoising protein expression data from droplet-based single cell profiling.
Springer Science and Business Media LLC
MetadataShow full item record
Mulè, M. P., Martins, A. J., & Tsang, J. S. (2022). Normalizing and denoising protein expression data from droplet-based single cell profiling.. Nat Commun, 13 (1) https://doi.org/10.1038/s41467-022-29356-8
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 ]".
Gene Expression Profiling, Single-Cell Analysis, Software
Intramural NIH HHS (ZIA AI001152)
External DOI: https://doi.org/10.1038/s41467-022-29356-8
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337374
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: email@example.com