BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.
While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profi...
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012742 |
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| author | Natalie R Davidson Fan Zhang Casey S Greene |
| author_facet | Natalie R Davidson Fan Zhang Casey S Greene |
| author_sort | Natalie R Davidson |
| collection | DOAJ |
| description | While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution. |
| format | Article |
| id | doaj-art-76be488e5f154aae952f8edb2afbdb71 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj-art-76be488e5f154aae952f8edb2afbdb712025-02-09T05:30:27ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101274210.1371/journal.pcbi.1012742BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.Natalie R DavidsonFan ZhangCasey S GreeneWhile single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.https://doi.org/10.1371/journal.pcbi.1012742 |
| spellingShingle | Natalie R Davidson Fan Zhang Casey S Greene BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. PLoS Computational Biology |
| title | BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. |
| title_full | BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. |
| title_fullStr | BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. |
| title_full_unstemmed | BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. |
| title_short | BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. |
| title_sort | buddi bulk deconvolution with domain invariance to predict cell type specific perturbations from bulk |
| url | https://doi.org/10.1371/journal.pcbi.1012742 |
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