Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders

• Verfasst von: Lukassen, Sören [VerfasserIn]
• Verfasst von: Ten, Foo Wei [VerfasserIn]
• Verfasst von: Adam, Lukas [VerfasserIn]
• Verfasst von: Eils, Roland [VerfasserIn]
• Verfasst von: Conrad, Christian [VerfasserIn]
• Titel: Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders
• Verf.angabe: Soeren Lukassen, Foo Wei Ten, Lukas Adam, Roland Eils and Christian Conrad
• E-Jahr: 2020
• Jahr: 07 December 2020
• Umfang: 19 S.
• Fussnoten: Gesehen am 08.02.2021
• Titel Quelle: Enthalten in: Nature machine intelligence
• Ort Quelle: [London] : Springer Nature Publishing, 2019
• Jahr Quelle: 2020
• Band/Heft Quelle: 2(2020), 12, Seite 800-819
• ISSN Quelle: 2522-5839
• DOI: doi:10.1038/s42256-020-00269-9
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 174775627X

Abstract

Recent advances in single-cell RNA sequencing have driven the simultaneous measurement of the expression of thousands of genes in thousands of single cells. These growing datasets allow us to model gene sets in biological networks at an unprecedented level of detail, in spite of heterogeneous cell populations. Here, we propose a deep neural network model that is a hybrid of matrix factorization and variational autoencoders, which we call restricted latent variational autoencoder (resVAE). The model uses weights as factorized matrices to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. This artificial neural network model seamlessly integrates functional gene set inference, experimental covariate effect isolation, and static gene identification, which we conceptually demonstrate here for four single-cell RNA sequencing datasets.