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

doi:10.1007/s40484-019-0192-7

Quantitative Biology

2020, Vol. 8

Issue (1): 78-94 https://doi.org/10.1007/s40484-019-0192-7

本期目录

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

Md. Bahadur Badsha¹, Rui Li¹, Boxiang Liu², Yang I. Li³, Min Xian⁴, Nicholas E. Banovich⁵, Audrey Qiuyan Fu¹(

)

¹. Department of Statistical Science, Institute for Bioinformatics and Evolutionary Studies, Institute for Modeling Collaboration & Innovation, University of Idaho, Moscow, ID 83844, USA
². Department of Biology, Stanford University, Stanford, CA 94305 , USA
³. Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
⁴. Department of Computer Science, University of Idaho, Idaho Falls, ID 83401, USA
⁵. The Translational Genomics Research Institute, Phoenix, AZ 85004, USA

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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.

Key words： single-cell gene expression deep learning autoencoder

收稿日期: 2019-08-01 出版日期: 2020-03-23

Corresponding Author(s): Audrey Qiuyan Fu

引用本文:

. [J]. Quantitative Biology, 2020, 8(1): 78-94.
Md. Bahadur Badsha, Rui Li, Boxiang Liu, Yang I. Li, Min Xian, Nicholas E. Banovich, Audrey Qiuyan Fu. Imputation of single-cell gene expression with an autoencoder neural network. Quant. Biol., 2020, 8(1): 78-94.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-019-0192-7
https://academic.hep.com.cn/qb/CN/Y2020/V8/I1/78

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