scPDA: denoising protein expression in droplet-based single-cell data
Abstract Droplet-based profiling techniques such as CITE-seq are often contaminated by technical noise. Current computational denoising methods have serious limitations, including a strong reliance on often-unavailable empty droplets or null controls and insufficient efficiency due to ignoring prote...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-025-03686-4 |
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| _version_ | 1849234714823491584 |
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| author | Ouyang Zhu Jun Li |
| author_facet | Ouyang Zhu Jun Li |
| author_sort | Ouyang Zhu |
| collection | DOAJ |
| description | Abstract Droplet-based profiling techniques such as CITE-seq are often contaminated by technical noise. Current computational denoising methods have serious limitations, including a strong reliance on often-unavailable empty droplets or null controls and insufficient efficiency due to ignoring protein-protein interactions. Here, we introduce scPDA, a probabilistic model that employs a variational autoencoder to achieve high computational efficiency. scPDA eliminates the use of empty droplets and shares information across proteins to increase denoising efficiency. Compared to currently available methods, scPDA substantially improves the efficiency of gating-strategy-based cell-type identification, marking a clear advancement in computational denoising of the protein modality. |
| format | Article |
| id | doaj-art-b0db49f2bc6445c19e6f8c3ccb854746 |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-b0db49f2bc6445c19e6f8c3ccb8547462025-08-20T04:03:02ZengBMCGenome Biology1474-760X2025-07-0126112410.1186/s13059-025-03686-4scPDA: denoising protein expression in droplet-based single-cell dataOuyang Zhu0Jun Li1Department of Applied and Computational Mathematics and Statistics, University of Notre DameDepartment of Applied and Computational Mathematics and Statistics, University of Notre DameAbstract Droplet-based profiling techniques such as CITE-seq are often contaminated by technical noise. Current computational denoising methods have serious limitations, including a strong reliance on often-unavailable empty droplets or null controls and insufficient efficiency due to ignoring protein-protein interactions. Here, we introduce scPDA, a probabilistic model that employs a variational autoencoder to achieve high computational efficiency. scPDA eliminates the use of empty droplets and shares information across proteins to increase denoising efficiency. Compared to currently available methods, scPDA substantially improves the efficiency of gating-strategy-based cell-type identification, marking a clear advancement in computational denoising of the protein modality.https://doi.org/10.1186/s13059-025-03686-4 |
| spellingShingle | Ouyang Zhu Jun Li scPDA: denoising protein expression in droplet-based single-cell data Genome Biology |
| title | scPDA: denoising protein expression in droplet-based single-cell data |
| title_full | scPDA: denoising protein expression in droplet-based single-cell data |
| title_fullStr | scPDA: denoising protein expression in droplet-based single-cell data |
| title_full_unstemmed | scPDA: denoising protein expression in droplet-based single-cell data |
| title_short | scPDA: denoising protein expression in droplet-based single-cell data |
| title_sort | scpda denoising protein expression in droplet based single cell data |
| url | https://doi.org/10.1186/s13059-025-03686-4 |
| work_keys_str_mv | AT ouyangzhu scpdadenoisingproteinexpressionindropletbasedsinglecelldata AT junli scpdadenoisingproteinexpressionindropletbasedsinglecelldata |