联合分析的多输入数据集 (MIASA):用于联合分析分离的变量集的框架
Nomenjanahary Alexia Raharinirina1,2, Vikram Sunkara3, Max von Kleist1,4
1Department of Mathematics & Computer Science, Freie Universität Berlin, Berlin, Germany.
PloS one
|May 10, 2024
概括
分析描述相同现象但测量不同变量的多个数据集是具有挑战性的. MIASA框架及其qEE-Transition方法允许使用多种交互数据进行联合分析,以改进聚类和分类.
科学领域:
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
- 生物信息学是一种生物信息学.
背景情况:
- 具有不连接的变量集 (例如mRNA和蛋白质水平) 的数据集的联合分析是困难的.
- 传统方法侧重于单个相互作用模式 (变异,共变),忽视外部知识.
- 现有的方法难以整合各种互动信息进行全面分析.
研究的目的:
- 介绍MIASA,这是一个整体框架,用于从不同的数据集中联合分析多个变量.
- 为非欧几里德数据结构开发一种新的定性欧几里德嵌入 (qEE-过渡).
- 通过整合各种互动信息,实现有效的集群和分类.
主要方法:
- 将多个交互数据 (相似性,关联性,距离) 组合成交互分数.
- 开发了定性欧几里德嵌入 (qEE-过渡) 来在一个共同的欧几里德空间中创建矢量表示.
- 将框架应用于模拟数据集 (分布家族,相关变量,基因相互作用模型) 进行验证.
主要成果:
- qEE - 过渡之后的分区聚类优于非聚合方法.
- 与基于超度的聚合方法相比,性能有所不同.
- 当使用qEE-Transition与监督和无监督机器学习时,观察到有希望的结果.
结论:
- MIASA框架提供了一个强大的方法,用于对多变量数据集的联合分析.
- qEE-Transition有效地处理非欧几里德数据结构,改善聚类和分类.
- 进一步开发距离分布模型对于捕捉复杂相互作用至关重要.
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