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

Viktoria Schuster, Anders Krogh

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, which use a variational approximation of the likelihood for inference.