Computational cell type identification (celltyping) is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity and gene regulatory mechanism. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. In this talk, I will present our recent method developments on supervised celltyping for scATAC-seq using scATAC-seq data as reference. We developed Cellcano, a novel computational method based on a two-round supervised learning algorithm. The method alleviates the distributional shift between reference and target data and significantly improves the prediction performance.