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

Abstract

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.

Graphical abstract

Keywords

single-cell / gene expression / deep learning / autoencoder

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DATA AVAILABILITY

Data sets used in this paper are published and publicly available at the following website:ĥ Mouse bone marrow data: GEO GSE72857 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72857).ĥ GTEx gene expression data (Gene TPMs) (gtexportal.org/home/datasets).ĥ Human PBMC and mouse brain data from 10XGenomics (support.10Xgenomics.com/single-cell-gene-expression/datasets).ĥ Mouse retina data: provided in the scVI software package (github.com/YosefLab/scVI/tree/master/tests/data); original data from GEO GSE81905(www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81905).

CODE AVAILABILITY

Our software is implemented in Python and builds on Google TensorFlow. It can be run on CPUs and GPUs. The source code is available at the website (github.com/audreyqyfu/LATE).

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-019-0192-7.

AUTHOR CONTRIBUTIONS

A.Q.F. conceived the project. R.L., B.L. and A.Q.F. developed the method. R.L., M.X. and A.Q.F. implemented the software. M.B.B., R.L. and A.Q.F. analyzed the data. M.B.B., R.L., B.L., Y.I.L., N.E.B. and A.Q.F. interpreted the results. A.Q.F. wrote the manuscript with input from other authors.

ACKNOWLEDGEMENTS

We acknowledge the extensive support on hardware and software provided by the Computational Resources Core (CRC) through the Institute for Bioinformatics & Evolutionary Studies at the University of Idaho, and in particular the assistance from Dr. Benjamin Oswald, the Director of the CRC. This research is supported by NIH R00HG007368 (to A.Q.F.) and partially by the NIH/NIGMS grant P20GM104420 to the Institute for Modeling Collaboration & Innovation at the University of Idaho.ƒThe Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The gene expression data used for the analyses described in this manuscript were obtained from the GTEx Portal on 04/11/2018.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Md. Bahadur Badsha, Rui Li, Boxiang Liu, Yang I. Li, Min Xian, Nicholas E. Banovich and Audrey Qiuyan Fu declare that they have no conflicts of interest.ƒThis article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature