Single-cell transcriptomics overcomes the limitations of conventional transcriptome methods by isolating and sequencing RNA from individual cells, thus capturing unique expression values for each cell. This technology allows unprecedented precision in observing the stochasticity and heterogeneity of gene expression within cells. However, single-cell RNA sequencing (scRNA-seq) experiments often fail to capture all cells and genes comprehensively, and single-modality data is insufficient to explain cell states and systemic changes. To address this, the integration of multi-source scRNA-seq and single-cell multi-modality data has emerged, enabling the construction of comprehensive cell atlases. These integration methods also facilitate the exploration of causal relationships and gene regulatory mechanisms across different modalities. This review summarizes the fundamental principles, applications, and value of these integration methods in revealing biological changes, and analyzes the advantages, disadvantages, and future directions of current approaches.