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.