Imputation of single-cell gene expression with an autoencoder neural network

Background: Single-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitatio...

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Bibliographic Details
Published in:Quantitative biology 2020-03, Vol.8 (1), p.78-94
Main Authors: Bahadur Badsha, Md, Li, Rui, Liu, Boxiang, Li, Yang I., Xian, Min, Banovich, Nicholas E., Qiuyan Fu, Audrey
Format: Article
Language:English
Subjects:
autoencoder
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
deep learning
Gene expression
Life Sciences
Mathematical and Computational Biology
Neural networks
Research Article
Ribonucleic acid
RNA
single-cell
Transfer learning
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Summary:Background: Single-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses. Methods: To overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates. Results: On both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU. Conclusions: We demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.
ISSN:2095-4689
2095-4697
DOI:10.1007/s40484-019-0192-7