缺少标签的共识聚类 (ccml):一种共识聚类工具,用于在具有不平等样本覆盖率的队列中进行多omics整合预测
Chuan-Xing Li1, Hongyan Chen2, Nazanin Zounemat-Kermani3,4
1Respiratory Medicine Unit, Department of Medicine Solna & Centre for Molecular Medicine, Karolinska Institutet.
Briefings in bioinformatics
|January 11, 2024
概括
我们开发了一种新的共识聚类方法 (ccml),以整合多omics数据,即使缺少样本. 这种方法可以改善COPD和喘等复杂疾病的患者亚组发现.
科学领域:
- 生物医学数据科学 生物医学数据科学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 多学科数据集成对于理解复杂疾病至关重要.
- 现有的共识聚类方法在缺少数据和不同的样本覆盖率方面扎.
- 准确的患者分层需要强大的方法来处理现实世界的数据限制.
研究的目的:
- 引入一个新的共识集群与缺失标签 (ccml) 策略.
- 为了实现有效的多主题数据集成,尽管样本覆盖率不平等和数据缺失.
- 为先进的生物医学数据分析提供灵活的R协议.
主要方法:
- 在R中开发了一种两步共识集群协议 (ccml).
- 根据样本覆盖范围调整的共识权重,以正常化缺少的数据.
- 在COPD (卡罗林斯卡COSMIC) 和喘 (U-BIOPRED) 中的24-omics数据中应用了ccml到9-omics数据.
主要成果:
- 在多个omics数据集中,ccml成功处理了不平等的缺失标签.
- 在COPD和喘队伍中确定了分子上不同的子组.
- 证明了ccml对于复杂疾病的综合分组的有用性.
结论:
- ccml是多omics集成的有效工具,克服了缺少数据的局限性.
- 该方法增强了类似性网络融合等算法的下游分析.
- ccml为研究人员分析复杂的人类队列数据提供了宝贵的资源.
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