scRNA-seq is a promising technology to perform unbiased, high-throughput, and high-resolution transcriptome analysis at single-cell resolution. The raw data usually suffers from noise and low quality, such as dropout events, which hinder downstream analysis. Thus, it is essential to improve the quality of single-cell data. Although many methods have been developed for denoising scRNA-seq data, the existing methods mainly focus on finding the relationship within the data itself without fully utilizing other datasets with annotated cell labels. Here, we proposed SCdenoise, a semi-supervised denoising method, to denoise unlabeled target data based on annotated cells in the reference datasets, which could utilize biological characteristics hidden in the high-quality reference datasets. Extensive downstream analyses showed that our method outperformed state-of-the-art methods on both simulated and real datasets for single-cell data analyses, including gene expression recovery, differential analysis, and clustering analysis. The source code is available at https://github.com/zhongfqi/SCdenoise-.