在单细胞数据集中发现与疾病相关的细胞
Erin Craig1, Timothy J Keyes1,2, Jolanda Sarno2,3,4
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Science advances
|August 27, 2025
概括
我们开发了混合模型多实例学习 (MMIL) 仅使用患者级疾病标签对细胞进行分类. 这种方法在单细胞数据中准确识别白血病细胞,包括最小残留病 (MRD).
科学领域:
- 计算生物学
- 机器学习
- 基因组学
背景情况:
- 单细胞数据集通常缺乏细胞特异性疾病标签,阻碍了准确的分类.
- 现有的方法难以识别未被标记的细胞和罕见疾病.
研究的目的:
- 引入混合模型多级学习 (MMIL) 用于仅使用患者级标签进行细胞分类.
- 在单细胞分析中开发一个强大的框架来利用标记和未标记的细胞.
主要方法:
- MMIL使用预期最大化算法来训练细胞级二进制分类器.
- 该方法使用患者级疾病状态标签来推断细胞级标签.
主要成果:
- 在初级患者样本中,MMIL能够准确地区分白血病细胞和正常细胞.
- 该方法成功识别了罕见的最小残留病 (MRD) 细胞.
- MMIL在不同组织和治疗时间点中表现出泛化,特征识别准确度接近血液病理学家的准确度.
结论:
- 在未知黄金标准细胞标签的情况下,MMIL提供了灵活而准确的细胞分类解决方案.
- 这种方法提高了单个单元数据的实用性,即使单元级注释有限,也可以进行可靠的分析.
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