已经开发出几种方法来通过计算预测单细胞 RNA 测序 (scRNAseq) 数据的细胞类型。随着方法的开发，研究人员面临的一个常见问题是确定他们应该应用于特定用例的最佳方法。为了应对这一挑战，我们提出了 CHAI（通过相似矩阵积分进行单细胞类型识别的共识聚类），这是一种用于 scRNAseq 聚类的群体智慧方法。 CHAI 提出了两种相互竞争的方法，它们聚合了七种最先进的聚类方法的聚类结果：CHAI-AvgSim 和 CHAI-SNF。 CHAI-AvgSim 和 CHAI-SNF 在多个基准测试数据集上展示了卓越的性能。此外，两种 CHAI 方法都优于最新的共识聚类方法 SAME 聚类。我们通过鉴定富含 CDH3 的前导肿瘤细胞簇来展示 CHAI 的实际用例。 CHAI 提供了一个多组学集成平台，我们证明 CHAI-SNF 在包含空间转录组数据时具有改进的性能。 CHAI 通过将最新且性能最佳的 scRNAseq 聚类算法纳入聚合框架，克服了之前的限制。它也是一个直观且易于定制的 R 包，用户可以将自己的聚类方法添加到管道中，或者仅向下选择他们想要用于聚类聚合的方法。这确保了随着更先进的聚类算法的开发，CHAI 作为通用框架仍然对社区有用。 CHAI 在 GitHub 上作为开源 R 包提供：https://github.com/lodimk2/chai。© 作者 2024。由牛津大学出版社出版。

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.© The Author(s) 2024. Published by Oxford University Press.