Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning

Abstract

Single-cell RNA sequencing (scRNA-seq) techniques for measuring gene expression in individual cells have developed rapidly. Recently, the identification of cell types in scRNA-seq analysis has been accomplished using deep learning. Most methods utilize a dataset containing cell-type labels to train the model and then apply this model to other datasets. However, the integration of multiple datasets leads to unexpected batch effects caused by differences in laboratories, experimenters, and sequencing techniques. As the batch effect interrupts the biological signal of interest, an effective batch correction method is essential. In this article, we present scUDAS, a cell-type prediction model for scRNA-seq that utilizes unsupervised domain adaptation and semi-supervised learning (SSL) to reduce the differences in distributions between datasets. First, we pretrain the proposed model based on the source dataset, which contained cell-type information. Subsequently, scUDAS is trained on the target dataset by leveraging adversarial training to align the distribution of the target dataset with that of the source dataset. Finally, scUDAS was retrained to improve its performance through SSL by leveraging both the source and target datasets with consistency regularization. scUDAS outperformed the other deep learning-based batch correction models by appropriately removing the batch effect. scUDAS is publicly available at https://github.com/cbi-bioinfo/scUDAS.