使用非负矩阵因数分解的"omics数据"的整合子类型分析的随机奇数值分解
Yonghui Ni1, Jianghua He1, Prabhakar Chalise1
1Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA.
Statistical applications in genetics and molecular biology
|November 8, 2023
概括
这项研究介绍了intNMF-rsvd,这是一种使用多omics数据发现癌症亚型的新方法. 它通过减少数据维度和计算时间,有效地识别子类型,帮助临床研究.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 多omics数据集成对于癌症亚型差异化至关重要.
- 像非负矩阵因子化 (NMF) 这样的矩阵因子化方法用于整合集群.
- 高维度和长的计算时间是多omics集群中的挑战.
研究的目的:
- 提出一种新的方法,intNMF-rsvd,用于高效的多主题数据集群.
- 解决癌症亚型分析中高维度和计算成本的挑战.
- 改进在多样化的奥米克数据集中识别潜伏子类型结构.
主要方法:
- 利用随机的奇数值分解 (RSVD) 来进行维度缩小.
- 应用非负矩阵因数分解 (NMF) 用于整合集群 (intNMF-rsvd).
- 将多个omics数据投射到具有用户指定的较低等级的自身向量空间中.
主要成果:
- 与最先进的方法相比,intNMF-rsvd表现出高效和具有竞争力的性能.
- 该方法有效地处理大量的功能,并大大减少了计算时间.
- 使用模拟和癌症基因组图谱 (TCGA) 数据集进行评估.
结论:
- intNMF-rsvd为多主题整合集群提供了一种高效的方法.
- 该方法减少计算时间的能力使其适用于大规模的癌症数据分析.
- 鉴定的亚型可以促进进一步的疾病病因学临床关联研究.
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