MetaQ: fast, scalable and accurate metacell inference via single-cell quantization

Yunfan Li, Hancong Li, Yijie Lin, Dan Zhang, Dezhong Peng, Xiting Liu, Jie Xie, Peng Hu, Lu Chen, Han Luo and Xi Peng ()
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Yunfan Li: Sichuan University
Hancong Li: Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Center for Disease Related Molecular Network, West China Hospital, Sichuan University
Yijie Lin: Sichuan University
Dan Zhang: West China Second University Hospital, Sichuan University
Dezhong Peng: Sichuan University
Xiting Liu: Georgia Insitute of Technology
Jie Xie: Sichuan Normal University
Peng Hu: Sichuan University
Lu Chen: West China Second University Hospital, Sichuan University
Han Luo: Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Center for Disease Related Molecular Network, West China Hospital, Sichuan University
Xi Peng: Sichuan University

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract To overcome the computational barriers of analyzing large-scale single-cell sequencing data, we introduce MetaQ, a metacell algorithm that scales to arbitrarily large datasets with linear runtime and constant memory usage. Inspired by cellular development, MetaQ conceptualizes each metacell as a collective ancestor of biologically similar cells. By quantizing cells into a discrete codebook, where each entry represents a metacell capable of reconstructing the original cells it quantizes, MetaQ identifies homogeneous cell subsets for efficient and accurate metacell inference. This approach reduces computational complexity from exponential to linear while maintaining or surpassing the performance of existing metacell algorithms. Extensive experiments demonstrate that MetaQ excels in downstream tasks such as cell type annotation, developmental trajectory inference, batch integration, and differential expression analysis. Thanks to its superior efficiency and effectiveness, MetaQ makes analyzing datasets with millions of cells practical, offering a powerful solution for single-cell studies in the era of high-throughput profiling.

Date: 2025
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DOI: 10.1038/s41467-025-56424-6

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