The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data

• URL: http://arxiv.org/abs/2110.06672v3
• Date: Wed, 12 Jul 2023 14:13:24 GMT
• Title: The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data
• Authors: Viktoria Schuster and Anders Krogh
• Abstract summary: We present a simple generative model that computes model parameters and representations directly via maximum a posteriori (MAP) estimation. The advantages of this approach are its simplicity and its capability to provide representations of much smaller dimensionality than a comparable VAE.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models such as variational autoencoders (VAEs) which use a variational approximation of the likelihood for inference. We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a posteriori (MAP) estimation. The DGD handles complex parameterized latent distributions naturally unlike VAEs which typically use a fixed Gaussian distribution, because of the complexity of adding other types. We first show its general functionality on a commonly used benchmark set, Fashion-MNIST. Secondly, we apply the model to multiple single-cell data sets. Here the DGD learns low-dimensional, meaningful and well-structured latent representations with sub-clustering beyond the provided labels. The advantages of this approach are its simplicity and its capability to provide representations of much smaller dimensionality than a comparable VAE.

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