Cargando…
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data
Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Exp...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277074/ https://www.ncbi.nlm.nih.gov/pubmed/34191792 http://dx.doi.org/10.1371/journal.pcbi.1009086 |
_version_ | 1783722012051505152 |
---|---|
author | Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred |
author_facet | Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred |
author_sort | Kopf, Andreas |
collection | PubMed |
description | Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its 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 generate realistic 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 a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods. |
format | Online Article Text |
id | pubmed-8277074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82770742021-07-20 Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred PLoS Comput Biol Research Article Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its 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 generate realistic 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 a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods. Public Library of Science 2021-06-30 /pmc/articles/PMC8277074/ /pubmed/34191792 http://dx.doi.org/10.1371/journal.pcbi.1009086 Text en © 2021 Kopf et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data |
title | Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data |
title_full | Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data |
title_fullStr | Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data |
title_full_unstemmed | Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data |
title_short | Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data |
title_sort | mixture-of-experts variational autoencoder for clustering and generating from similarity-based representations on single cell data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277074/ https://www.ncbi.nlm.nih.gov/pubmed/34191792 http://dx.doi.org/10.1371/journal.pcbi.1009086 |
work_keys_str_mv | AT kopfandreas mixtureofexpertsvariationalautoencoderforclusteringandgeneratingfromsimilaritybasedrepresentationsonsinglecelldata AT fortuinvincent mixtureofexpertsvariationalautoencoderforclusteringandgeneratingfromsimilaritybasedrepresentationsonsinglecelldata AT somnathvigneshram mixtureofexpertsvariationalautoencoderforclusteringandgeneratingfromsimilaritybasedrepresentationsonsinglecelldata AT claassenmanfred mixtureofexpertsvariationalautoencoderforclusteringandgeneratingfromsimilaritybasedrepresentationsonsinglecelldata |