Table of Links
Abstract and 1. Introduction
2. Background
2.1 Amortized Stochastic Variational Bayesian GPLVM
2.2 Encoding Domain Knowledge through Kernels
3. Our Model and Pre-Processing and Likelihood
3.2 Encoder
4. Results and Discussion and 4.1 Each Component is Crucial to Modifies Model Performance
4.2 Modified Model achieves Significant Improvements over Standard Bayesian GPLVM and is Comparable to SCVI
4.3 Consistency of Latent Space with Biological Factors
4. Conclusion, Acknowledgement, and References
A. Baseline Models
B. Experiment Details
C. Latent Space Metrics
D. Detailed Metrics
5 CONCLUSION
This paper identifies a misalignment in the generative model of current GPLVMs used in single-cell data and proposes an amortized BGPLVM better adapted to the scRNA-seq dimensionality reduction setting. In particular, by drawing insight from commonly used single-cell-specific methods, including scVI, LDVAE, and Splatter single-cell simulations, our proposed model tackles three main aspects of single-cell data by (1) accounting for count data with an approximate Poisson likelihood, (2) incorporating batch effect modelling in both the encoder and GP kernel, and (3) normalizing the library size in the data via a pre-processing step. We demonstrate the importance of aligning modelling choices to domain-specific knowledge as the model achieves comparable performance to scVI on both a simulated dataset and real-world COVID dataset in both UMAP visualizations and commonly used latent space metrics.
ACKNOWLEDGMENTS
The authors would like to thank Emma Dann, Natsuhiko Kumasaka and the rest of the team at Sanger for help and guidance with our initial project and for providing the data and code, which we based this study on. AR is supported by the accelerate programme for scientific discovery. During the time of this work, SZ was supported by the Churchill Scholarship.
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Authors:
(1) Sarah Zhao, Department of Statistics, Stanford University, ([email protected]);
(2) Aditya Ravuri, Department of Computer Science, University of Cambridge ([email protected]);
(3) Vidhi Lalchand, Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard ([email protected]);
(4) Neil D. Lawrence, Department of Computer Science, University of Cambridge ([email protected]).
