scVI with Romain Lopez and Gabriel Misrachi (#36)
August 30, 2019
In this episode, we hear from Romain Lopez and Gabriel Misrachi about scVI—Single-cell Variational Inference. scVI is a probabilistic model for single-cell gene expression data that combines a hierarchical Bayesian model with deep neural networks encoding the conditional distributions. scVI scales to over one million cells and can be used for scRNA-seq normalization and batch effect removal, dimensionality reduction, visualization, and differential expression. We also discuss the recently implemented in scVI automatic hyperparameter selection via Bayesian optimization.
- Deep generative modeling for single-cell transcriptomics (Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, Nir Yosef)
- scVI on GitHub
- Should we zero-inflate scVI?
- Hyperparameter search for scVI
- Droplet scRNA-seq is not zero inflated (Valentine Svensson)
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