AAE-SC: A scRNA-seq Clustering Framework based on Adversarial Autoencoder

Yulun Wu, Yanming Guo, Yandong Xiao, Songyang Lao
2020 IEEE Access  
Single-cell RNA sequencing (scRNA-seq) provides the expression profiles of individual cells, and it is expected to provide higher cellular differential resolution than traditional bulk RNA sequencing. In scRNA-seq analysis, clustering is crucial for identifying cell types, and can be potentially exploited to understand high-level biological processes. Recently, autoencoder has been successfully applied in scRNAseq clustering problem and achieved promising results. Most existing works focus on
more » ... aracterizing the sparsity of data, and directly utilize the bottleneck feature of the autoencoder for clustering might not be optimal. In this paper, a novel framework named Adversarial AutoEncoder ScRNA-seq Clustering (AAE-SC) is proposed to bring an additional constraint on the bottleneck feature. Specifically, AAE-SC adds an AAE module on top of the bottleneck layer, and constrains the bottleneck feature distribution to be aligned with a consistent distribution. Also, the AAE and the reconstructed modules are jointly optimized to generate a highly discriminative and consistent feature, which is further proceeded for clustering. We find that by using AAE-SC to impose certain constraints on the features of the hidden layer, the performance of clustering can be improved. Experimental results on three real-world datasets demonstrate that the proposed AAE-SC framework outperformed state-of-the-art methods by 2% at least and 5% at most. And AAE-SC shows more robustness than the baseline model for downsampled and unbalanced cluster size datasets.
doi:10.1109/access.2020.3027481 fatcat:agl6cbzjdvborji5dr3spxrfn4