Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data

Nan Miles Xi; Jingyi Jessica Li

doi:10.1016/j.csbj.2023.07.041

Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data

Comput Struct Biotechnol J. 2023 Aug 4:21:4079-4095. doi: 10.1016/j.csbj.2023.07.041. eCollection 2023.

Authors

Nan Miles Xi¹, Jingyi Jessica Li^{2

3

4

5}

Affiliations

¹ Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL 60660, USA.
² Department of Statistics and Data Science, University of California, Los Angeles, CA 90095-1554, USA.
³ Department of Human Genetics, University of California, Los Angeles, CA 90095-7088, USA.
⁴ Department of Computational Medicine, University of California, Los Angeles, CA 90095-1766, USA.
⁵ Department of Biostatistics, University of California, Los Angeles, CA 90095-1772, USA.

Abstract

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

Keywords: Autoencoder design; Benchmark; Data imputation; ScRNA-seq.