Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data

TL;DR

The paper introduces MoE-Sim-VAE, a novel generative clustering model that combines a Variational Autoencoder with a Mixture-of-Experts architecture to effectively learn multi-modal distributions and improve clustering and data generation in high-dimensional biological data.

Contribution

It presents a new MoE-Sim-VAE model that enhances clustering and generative capabilities for high-dimensional data using a mixture-of-experts VAE with similarity-based latent representations.

Findings

Superior clustering performance on MNIST, single-cell RNA-seq, and CyTOF datasets.

Effective learning of multi-modal data distributions.

Improved data generation quality.

Abstract

Clustering high-dimensional data, such as images or biological measurements, is a long-standingproblem and has been studied extensively. Recently, Deep Clustering has gained popularity due toits flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model.The model can learn multi-modal distributions of high-dimensional data and use these to generaterealistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder(VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts.Additionally, we encourage the lower dimensional latent representation of our model to follow aGaussian mixture distribution and to accurately represent the similarities between the data points. Weassess the performance of our model on the MNIST benchmark data set and challenging real-worldtasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cellsubpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets.MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to thebaselines as well as competitor methods.