Kidney organoids provide a valuable resource to understand kidney development and disease. Clustering algorithms and marker genes fail to accurately and robustly classify cellular identity between human pluripotent stem cell (hPSC)-derived organoid datasets. Here we present a new method able to accurately classify kidney cell subtypes, a hierarchical machine learning model trained using comprehensive reference data from single cell RNA-sequencing of human fetal kidney (HFK). We demonstrate the tool's (DevKidCC) performance by application to all published kidney organoid datasets and a novel dataset. DevKidCC is available on Github and can be used on any kidney single cell RNA-sequence data.

中文翻译：

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DevKidCC可以对肾脏类器官数据集进行可靠的分类和直接比较

肾脏类器官提供了了解肾脏发育和疾病的宝贵资源。聚类算法和标记基因无法准确，可靠地对人类多能干细胞（hPSC）衍生的类器官数据集之间的细胞身份进行分类。在这里，我们提出了一种能够准确分类肾细胞亚型的新方法，这是一种使用来自人类胎儿肾脏（HFK）的单细胞RNA测序的综合参考数据训练的分层机器学习模型。我们通过将其应用于所有已发布的肾脏类器官数据集和一个新颖的数据集来演示该工具（DevKidCC）的性能。DevKidCC在Github上可用，可用于任何肾脏单细胞RNA序列数据。