Clustering is widely used for single-cell analysis, but current methods are limited in accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method that outperforms other methods at hard clustering without the need for hyperparameter tuning. It also performs soft clustering to characterize continuum-like regions and quantify clustering uncertainty, demonstrated here by mapping the connectivity and intermediate transitions between MNIST handwritten digits and between hypothalamic tanycyte subpopulations. This hyperparameter-randomized ensemble approach improves the accuracy, robustness, ease of use, and interpretability of single-cell clustering, and may prove useful in other fields as well.

中文翻译：

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ESCHR：一种超参数随机集成方法，用于跨不同数据集的稳健聚类


聚类广泛用于单细胞分析，但当前的方法在准确性、稳健性、易用性和可解释性方面受到限制。为了解决这些限制，我们开发了一种集成聚类方法，该方法在硬聚类方面优于其他方法，而无需进行超参数调整。它还执行软聚类来表征类似连续体的区域并量化聚类不确定性，此处通过映射 MNIST 手写数字之间和下丘脑单细胞亚群之间的连接性和中间转换来演示。这种超参数随机集成方法提高了单细胞聚类的准确性、鲁棒性、易用性和可解释性，并且可能在其他领域也很有用。