TY - JOUR

T1 - The Deep Generative Decoder

T2 - MAP estimation of representations improves modelling of single-cell RNA data

AU - Schuster, Viktoria

AU - Krogh, Anders

N1 - Publisher Copyright: © 2023 The Author(s). Published by Oxford University Press.

PY - 2023

Y1 - 2023

N2 - 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, which use a variational approximation of the likelihood for inference. Results: We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a posteriori estimation. The DGD handles complex parameterized latent distributions naturally unlike variational autoencoders, 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 datasets. 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 variational autoencoder.

AB - 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, which use a variational approximation of the likelihood for inference. Results: We here present the Deep Generative Decoder (DGD), a simple generative model that computes model parameters and representations directly via maximum a posteriori estimation. The DGD handles complex parameterized latent distributions naturally unlike variational autoencoders, 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 datasets. 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 variational autoencoder.

UR - http://www.scopus.com/inward/record.url?scp=85172672094&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btad497

DO - 10.1093/bioinformatics/btad497

M3 - Journal article

C2 - 37572301

AN - SCOPUS:85172672094

VL - 39

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

SN - 1367-4811

IS - 9

M1 - btad497

ER -