#36 scVI with Romain Lopez and Gabriel Misrachi

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.





Links:

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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