The article introduces SnapATAC2, a Python package designed for the analysis of single-cell omics data. SnapATAC2 addresses the computational challenges of projecting large-scale, high-dimensional data into low-dimensional space while retaining relative cell relationships. The key innovation is a nonlinear dimensionality reduction algorithm that uses a matrix-free spectral embedding approach, which is more efficient and accurate than traditional methods. This algorithm scales linearly with the number of cells, making it suitable for diverse single-cell datasets, including single-cell RNA sequencing, chromatin accessibility, and multi-omics data. SnapATAC2 outperforms existing methods in terms of speed, scalability, and precision, demonstrating robustness to noise and varying sequencing depths. The package is versatile, integrating with other software tools and supporting various data types, making it a powerful tool for advancing single-cell analysis.The article introduces SnapATAC2, a Python package designed for the analysis of single-cell omics data. SnapATAC2 addresses the computational challenges of projecting large-scale, high-dimensional data into low-dimensional space while retaining relative cell relationships. The key innovation is a nonlinear dimensionality reduction algorithm that uses a matrix-free spectral embedding approach, which is more efficient and accurate than traditional methods. This algorithm scales linearly with the number of cells, making it suitable for diverse single-cell datasets, including single-cell RNA sequencing, chromatin accessibility, and multi-omics data. SnapATAC2 outperforms existing methods in terms of speed, scalability, and precision, demonstrating robustness to noise and varying sequencing depths. The package is versatile, integrating with other software tools and supporting various data types, making it a powerful tool for advancing single-cell analysis.