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. 2022 Oct 26;17(3):173902. doi: 10.1007/s11704-022-2011-y

AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction

Shuchang Zhao 1,2, Li Zhang 1,3, Xuejun Liu 1,2,✉
PMCID: PMC9607720  PMID: 36320820

Abstract

Single-cell RNA sequencing (scRNA-seq) technology has become an effective tool for high-throughout transcriptomic study, which circumvents the averaging artifacts corresponding to bulk RNA-seq technology, yielding new perspectives on the cellular diversity of potential superficially homogeneous populations. Although various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material, the technical noise and biological variation are inevitably introduced into experimental process, resulting in high dropout events, which greatly hinder the downstream analysis. Considering the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data, we propose a customized autoencoder based on a two-part-generalized-gamma distribution (AE-TPGG) for scRNA-seq data analysis, which takes mixed discrete-continuous random variables of scRNA-seq data into account using a two-part model and utilizes the generalized gamma (GG) distribution, for fitting the positive and right-skewed continuous data. The adopted autoencoder enables AE-TPGG to captures the inherent relationship between genes. In addition to the ability of achieving low-dimensional representation, the AE-TPGG model also provides a denoised imputation according to statistical characteristic of gene expression. Results on real datasets demonstrate that our proposed model is competitive to current imputation methods and ameliorates a diverse set of typical scRNA-seq data analyses.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at 10.1007/s11704-022-2011-y.

Keywords: scRNA-seq, autoencoder, TPGG, data imputation, dimensionality reduction

Electronic Supplementary Material

11704_2022_2011_MOESM1_ESM.pdf (476.4KB, pdf)

AE-TPGG: A Novel Autoencoder-Based Approach for Single-cell RNA-seq Data Imputation and Dimensionality Reduction

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 62136004, 61802193), the National Key R&D Program of China (2018YFC2001600, 2018YFC2001602), the Natural Science Foundation of Jiangsu Province (BK20170934), and the Fundamental Research Funds for the Central Universities (NJ2020023). Thanks to all the open-minded researchers providing the codes and research resources. Thanks to all the anonymous reviewers.

Footnotes

Shuchang Zhao received the BSc degree from the Suzhou University, China in 2013, and MSc degree from the Anhui University of Science and Technology, China in 2016, respectively. Currently, he is working toward the PhD degree in the PARNEC group of the College of Computer Science and Technology at Nanjing University of Aeronautics and Astronautics (NUAA), China. His research interests include machine learning and bioinformatics.

Li Zhang received the BSc degree from the Changsha University of Science and Technology, China in 2007, and the MSc and PhD degrees from the Nanjing University of Aeronautics and Astronautics (NUAA), China in 2010 and 2015, respectively. He joined the College of Computer Science and Technology, Nanjing Forestry University, as a Lecturer, China in 2016. His current research interests include machine learning and bioinformatics.

Xuejun Liu received the BSc and MSc degrees in computer science from the Nanjing University of Aeronautics and Astronautics (NUAA), China in 1999 and 2002, respectively, and the PhD degree in computer science from the University of Manchester, UK in 2006. Currently, she is a professor in the PARNEC group of the College of Computer Science and Technology at NUAA, China. Her research interests include machine learning and its practical applications, including bioinformatics.

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Supplementary Materials

11704_2022_2011_MOESM1_ESM.pdf (476.4KB, pdf)

AE-TPGG: A Novel Autoencoder-Based Approach for Single-cell RNA-seq Data Imputation and Dimensionality Reduction

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