美国格莱斯顿学院M. Ryan Corces小组在研究中取得进展。他们揭示了CHOIR改进了基于单细胞数据的细胞类型和状态的显著性检测。相关论文于2025年4月7日发表于国际顶尖学术期刊《自然—遗传学》杂志上。

为了解决这些挑战，研究小组提出了CHOIR（迭代随机森林分层优化），它应用随机森林分类器框架和跨分层分层树的排列测试来统计地确定代表不同种群的分层树。该课题组研究人员通过对15种现有的聚类方法进行广泛的基准测试，通过230种模拟和5种真实的单细胞RNA测序，转座酶可及的染色质测序，空间转录组学和多组学数据集，证明了CHOIR的性能。CHOIR可以应用于任何单细胞数据类型，并提供了一个灵活的，可扩展的和机器人的解决方案，以识别异质单细胞数据内的生物相关细胞组的挑战。

据了解，聚类是分析单细胞数据的关键步骤，可以发现和表征细胞类型和状态。然而，大多数流行的细胞分离工具没有对结果进行统计推断测试，导致细胞分离过度或细胞分离不足的风险，并且经常导致对流行率差异很大的细胞类型的无效识别。

附：英文原文

Title: CHOIR improves significance-based detection of cell types and states from single-cell data

Author: Sant, Cathrine, Mucke, Lennart, Corces, M. Ryan

Issue&Volume: 2025-04-07

Abstract: Clustering is a critical step in the analysis of single-cell data, enabling the discovery and characterization of cell types and states. However, most popular clustering tools do not subject results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (cluster hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine clusters representing distinct populations. We demonstrate the performance of CHOIR through extensive benchmarking against 15 existing clustering methods across 230 simulated and five real single-cell RNA sequencing, assay for transposase-accessible chromatin sequencing, spatial transcriptomic and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable and robust solution to the challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.

DOI: 10.1038/s41588-025-02148-8

Source: https://www.nature.com/articles/s41588-025-02148-8