Single cell transcriptomics is revolutionising our understanding of tissue and disease heterogeneity, yet cell type identification remains a partially manual task. Published algorithms for automatic cell annotation are limited to known cell types and fail to capture novel populations, especially cancer cells. We developed northstar, a computational approach to classify thousands of cells based on published data within seconds while simultaneously identifying and highlighting new cell states such as malignancies. We tested northstar on data from glioblastoma, melanoma, and seven different healthy tissues and obtained high accuracy and robustness. We collected eleven pancreatic tumors and identified three shared and five private neoplastic cell populations, offering insight into the origins of neuroendocrine and exocrine tumors. Northstar is a useful tool to assign known and novel cell type and states in the age of cell atlases.

单细胞转录组学正在彻底改变我们对组织和疾病异质性的理解，但细胞类型识别仍然是部分手动任务。已发布的自动细胞注释算法仅限于已知的细胞类型，无法捕获新的细胞群，尤其是癌细胞。我们开发了 Northstar，这是一种计算方法，可以在几秒钟内根据已发布的数据对数千个细胞进行分类，同时识别和突出显示恶性肿瘤等新的细胞状态。我们使用来自胶质母细胞瘤、黑色素瘤和七种不同健康组织的数据测试了 Northstar，并获得了高精度和稳健性。我们收集了 11 个胰腺肿瘤，并鉴定了 3 个共享肿瘤细胞群和 5 个私有肿瘤细胞群，从而深入了解神经内分泌和外分泌肿瘤的起源。 Northstar 是在细胞图谱时代分配已知和新颖的细胞类型和状态的有用工具。