AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction
Shuchang ZHAO1,2, Li ZHANG1,3, Xuejun LIU1,2()
1. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China 2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China 3. College of Computer Science and Technology, Nanjing Forestry University, Nanjing 210037, China
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.
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