ArticleNature communications2025
MetaQ: fast, scalable and accurate metacell inference via single-cell quantization.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- scHashFormer: a hash-driven graph transformer for scalable scRNA-seq clustering.Briefings in bioinformatics · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Linking Genetic Risk to Disease-Relevant Cellular States via Metacell-Informed Modeling with ICePop.bioRxiv : the preprint server for biology · 2026Article
- Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck.BMC biology · 2026Article
- Library size-stabilized metacells construction enhances co-expression network analysis in single-cell data.PLoS computational biology · 2025Article
- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.Nature communications · 2025Article
- MetaQ: fast, scalable and accurate metacell inference via single-cell quantization.Nature communications · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.