Deep learning enables accurate alignment of single cell RNA-seq data

Yuanke Zhong, Jing Li, Jie Liu, Yan Zheng, Xuequn Shang, Jialu Hu

Abstract: As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed a single cell alignment method using deep autoencoder followed by k-nearst-neighbor cells (scadKNN), which learns the feature representation of the data while eliminating batch effects and dropouts through deep autoencoder and uses the low-dimensional feature to align cell types, thereby reducing calculation effort and improving alignment accuracy. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.