تقرير
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
| العنوان: | Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder |
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| المؤلفون: | Bereket, Michael, Karaletsos, Theofanis |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Quantitative Biology Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods |
| الوصف: | Generative models of observations under interventions have been a vibrant topic of interest across machine learning and the sciences in recent years. For example, in drug discovery, there is a need to model the effects of diverse interventions on cells in order to characterize unknown biological mechanisms of action. We propose the Sparse Additive Mechanism Shift Variational Autoencoder, SAMS-VAE, to combine compositionality, disentanglement, and interpretability for perturbation models. SAMS-VAE models the latent state of a perturbed sample as the sum of a local latent variable capturing sample-specific variation and sparse global variables of latent intervention effects. Crucially, SAMS-VAE sparsifies these global latent variables for individual perturbations to identify disentangled, perturbation-specific latent subspaces that are flexibly composable. We evaluate SAMS-VAE both quantitatively and qualitatively on a range of tasks using two popular single cell sequencing datasets. In order to measure perturbation-specific model-properties, we also introduce a framework for evaluation of perturbation models based on average treatment effects with links to posterior predictive checks. SAMS-VAE outperforms comparable models in terms of generalization across in-distribution and out-of-distribution tasks, including a combinatorial reasoning task under resource paucity, and yields interpretable latent structures which correlate strongly to known biological mechanisms. Our results suggest SAMS-VAE is an interesting addition to the modeling toolkit for machine learning-driven scientific discovery. Comment: Presented at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (Post-NeurIPS fixes: cosmetic fixes, updated references, added simulation to appendix) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.02794 |
| رقم الأكسشن: | edsarx.2311.02794 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |