scCMP: A Deep Learning Method for Identifying Clonal Mutational Profiles From Single-Cell Genomic Data

Accurately inferring clonal mutational profiles is essential for understanding intra-tumor heterogeneity and clonal selection during tumor evolution. Single-cell multi-modal genomic data, such as copy numbers and point mutations, can be integrated to deliver multiple views of the clonal mutational patterns. Despite of the fact that integration of single-cell multi-modal data has been extensively explored in existing studies, computational methods specifically developed to integrate copy number and point mutation data of single cells are still highly needed. We introduce a deep joint representation learning framework called scCMP, to accurately identify clonal mutational profiles. scCMP employs hybrid Transformer-CNN architectures and graph convolutional networks to integrate single-cell copy number and point mutation data. By fusing individual and commonality information among the two modalities, it generates meaningful cell embeddings for identifying clonal clusters. We comprehensively evaluate the effectiveness of scCMP on five real single-cell DNA sequencing datasets, and further showcase its good scalability on datasets generated from other omics technologies. The results show scCMP accurately aggregates the cells with similar mutational profiles into a same cluster, and surpasses the state-of-the-art methods, indicating its advantage in integrating single-cell genomic data.