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

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 p...

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Bibliographic Details
Published in:Nature machine intelligence Vol. 2; no. 12; pp. 800 - 809
Main Authors: Lukassen, Soeren, Ten, Foo Wei, Adam, Lukas, Eils, Roland, Conrad, Christian
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.12.2020
Nature Publishing Group
Subjects:
631/114/1305
631/114/2114
631/114/2785
Algorithms
Artificial neural networks
Bias
Coders
Datasets
Decomposition
Engineering
Factorization
Gene expression
Gene sequencing
Inference
Neural networks
Performance evaluation
Ribonucleic acid
RNA
ISSN:2522-5839
Online Access:Get full text
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Summary: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. The wealth of data generated by single-cell RNA sequencing can be used to identify gene sets across cells, as well as to identify specific cells. Lukassen and colleagues propose a method combining matrix factorization and variational auto encoders that can capture both cross-cell and cell-specific information.
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ISSN:2522-5839
DOI:10.1038/s42256-020-00269-9