A generative framework using latent heteroscedastic Gaussian process approximated via Hilbert space methods plus optimal transport to model population trends and infer trajectories in temporal scRNA-seq data.
Advances in neural information processing systems , volume=
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A tractable estimator for functional KL divergence provides a coherent way to compare trajectory inference methods and reveal discrepancies in inferred dynamics from snapshot data.
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Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
A generative framework using latent heteroscedastic Gaussian process approximated via Hilbert space methods plus optimal transport to model population trends and infer trajectories in temporal scRNA-seq data.
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A tractable estimator for functional KL divergence provides a coherent way to compare trajectory inference methods and reveal discrepancies in inferred dynamics from snapshot data.
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