Table of Links
2. Background
2.1 Amortized Stochastic Variational Bayesian GPLVM
2.2 Encoding Domain Knowledge through Kernels
3. Our Model and Pre-Processing and Likelihood
4. Results and Discussion and 4.1 Each Component is Crucial to Modifies Model Performance
4.3 Consistency of Latent Space with Biological Factors
4. Conclusion, Acknowledgement, and References
3.2 ENCODER
In the encoder analysis, we compare a simple encoder comprised of linear layers followed by SoftPlus activations (Simple NN) with the scVI’s more complex encoder (scVI NN). scVI NN incorporates batch information as input to the nonlinear mapping, so incorporating this encoder into the BGPLVM may help address batch effects observed in the raw count data. Additionally, the scVI encoder architecture includes batch normalizations, contributing to a more stable optimization process, which we leverage for our GPLVM implementation.
This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Sarah Zhao, Department of Statistics, Stanford University, (smxzhao@stanford.edu);
(2) Aditya Ravuri, Department of Computer Science, University of Cambridge (ar847@cam.ac.uk);
(3) Vidhi Lalchand, Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard (vidrl@mit.edu);
(4) Neil D. Lawrence, Department of Computer Science, University of Cambridge (ndl21@cam.ac.uk).